A fundamental role for context in instrumental learning and extinction.

PubMed

Bouton, Mark E; Todd, Travis P

2014-05-01

The purpose of this article is to review recent research that has investigated the effects of context change on instrumental (operant) learning. The first part of the article discusses instrumental extinction, in which the strength of a reinforced instrumental behavior declines when reinforcers are withdrawn. The results suggest that extinction of either simple or discriminated operant behavior is relatively specific to the context in which it is learned: As in prior studies of Pavlovian extinction, ABA, ABC, and AAB renewal effects can all be observed. Further analysis supports the idea that the organism learns to refrain from making a specific response in a specific context, or in more formal terms, an inhibitory context-response association. The second part of the article then discusses research suggesting that the context also controls instrumental behavior before it is extinguished. Several experiments demonstrate that a context switch after either simple or discriminated operant training causes a decrement in the strength of the response. Over a range of conditions, the animal appears to learn a direct association between the context and the response. Under some conditions, it can also learn a hierarchical representation of context and the response-reinforcer relation. Extinction is still more context-specific than conditioning, as indicated by ABC and AAB renewal. Overall, the results establish that the context can play a significant role in both the acquisition and extinction of operant behavior. Copyright © 2014 Elsevier B.V. All rights reserved.

How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis

PubMed Central

Collins, Anne G. E.; Frank, Michael J.

2012-01-01

Instrumental learning involves corticostriatal circuitry and the dopaminergic system. This system is typically modeled in the reinforcement learning (RL) framework by incrementally accumulating reward values of states and actions. However, human learning also implicates prefrontal cortical mechanisms involved in higher level cognitive functions. The interaction of these systems remains poorly understood, and models of human behavior often ignore working memory (WM) and therefore incorrectly assign behavioral variance to the RL system. Here we designed a task that highlights the profound entanglement of these two processes, even in simple learning problems. By systematically varying the size of the learning problem and delay between stimulus repetitions, we separately extracted WM-specific effects of load and delay on learning. We propose a new computational model that accounts for the dynamic integration of RL and WM processes observed in subjects' behavior. Incorporating capacity-limited WM into the model allowed us to capture behavioral variance that could not be captured in a pure RL framework even if we (implausibly) allowed separate RL systems for each set size. The WM component also allowed for a more reasonable estimation of a single RL process. Finally, we report effects of two genetic polymorphisms having relative specificity for prefrontal and basal ganglia functions. Whereas the COMT gene coding for catechol-O-methyl transferase selectively influenced model estimates of WM capacity, the GPR6 gene coding for G-protein-coupled receptor 6 influenced the RL learning rate. Thus, this study allowed us to specify distinct influences of the high-level and low-level cognitive functions on instrumental learning, beyond the possibilities offered by simple RL models. PMID:22487033

Rats bred for helplessness exhibit positive reinforcement learning deficits which are not alleviated by an antidepressant dose of the MAO-B inhibitor deprenyl.

PubMed

Schulz, Daniela; Henn, Fritz A; Petri, David; Huston, Joseph P

2016-08-04

Principles of negative reinforcement learning may play a critical role in the etiology and treatment of depression. We examined the integrity of positive reinforcement learning in congenitally helpless (cH) rats, an animal model of depression, using a random ratio schedule and a devaluation-extinction procedure. Furthermore, we tested whether an antidepressant dose of the monoamine oxidase (MAO)-B inhibitor deprenyl would reverse any deficits in positive reinforcement learning. We found that cH rats (n=9) were impaired in the acquisition of even simple operant contingencies, such as a fixed interval (FI) 20 schedule. cH rats exhibited no apparent deficits in appetite or reward sensitivity. They reacted to the devaluation of food in a manner consistent with a dose-response relationship. Reinforcer motivation as assessed by lever pressing across sessions with progressively decreasing reward probabilities was highest in congenitally non-helpless (cNH, n=10) rats as long as the reward probabilities remained relatively high. cNH compared to wild-type (n=10) rats were also more resistant to extinction across sessions. Compared to saline (n=5), deprenyl (n=5) reduced the duration of immobility of cH rats in the forced swimming test, indicative of antidepressant effects, but did not restore any deficits in the acquisition of a FI 20 schedule. We conclude that positive reinforcement learning was impaired in rats bred for helplessness, possibly due to motivational impairments but not deficits in reward sensitivity, and that deprenyl exerted antidepressant effects but did not reverse the deficits in positive reinforcement learning. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Reinforcement learning or active inference?

PubMed

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Chaos in learning a simple two-person game

PubMed Central

Sato, Yuzuru; Akiyama, Eizo; Farmer, J. Doyne

2002-01-01

We investigate the problem of learning to play the game of rock–paper–scissors. Each player attempts to improve her/his average score by adjusting the frequency of the three possible responses, using reinforcement learning. For the zero sum game the learning process displays Hamiltonian chaos. Thus, the learning trajectory can be simple or complex, depending on initial conditions. We also investigate the non-zero sum case and show that it can give rise to chaotic transients. This is, to our knowledge, the first demonstration of Hamiltonian chaos in learning a basic two-person game, extending earlier findings of chaotic attractors in dissipative systems. As we argue here, chaos provides an important self-consistency condition for determining when players will learn to behave as though they were fully rational. That chaos can occur in learning a simple game indicates one should use caution in assuming real people will learn to play a game according to a Nash equilibrium strategy. PMID:11930020

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

Preliminary Work for Examining the Scalability of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Clouse, Jeff

1998-01-01

Researchers began studying automated agents that learn to perform multiple-step tasks early in the history of artificial intelligence (Samuel, 1963; Samuel, 1967; Waterman, 1970; Fikes, Hart & Nilsonn, 1972). Multiple-step tasks are tasks that can only be solved via a sequence of decisions, such as control problems, robotics problems, classic problem-solving, and game-playing. The objective of agents attempting to learn such tasks is to use the resources they have available in order to become more proficient at the tasks. In particular, each agent attempts to develop a good policy, a mapping from states to actions, that allows it to select actions that optimize a measure of its performance on the task; for example, reducing the number of steps necessary to complete the task successfully. Our study focuses on reinforcement learning, a set of learning techniques where the learner performs trial-and-error experiments in the task and adapts its policy based on the outcome of those experiments. Much of the work in reinforcement learning has focused on a particular, simple representation, where every problem state is represented explicitly in a table, and associated with each state are the actions that can be chosen in that state. A major advantage of this table lookup representation is that one can prove that certain reinforcement learning techniques will develop an optimal policy for the current task. The drawback is that the representation limits the application of reinforcement learning to multiple-step tasks with relatively small state-spaces. There has been a little theoretical work that proves that convergence to optimal solutions can be obtained when using generalization structures, but the structures are quite simple. The theory says little about complex structures, such as multi-layer, feedforward artificial neural networks (Rumelhart & McClelland, 1986), but empirical results indicate that the use of reinforcement learning with such structures is promising. These empirical results make no theoretical claims, nor compare the policies produced to optimal policies. A goal of our work is to be able to make the comparison between an optimal policy and one stored in an artificial neural network. A difficulty of performing such a study is finding a multiple-step task that is small enough that one can find an optimal policy using table lookup, yet large enough that, for practical purposes, an artificial neural network is really required. We have identified a limited form of the game OTHELLO as satisfying these requirements. The work we report here is in the very preliminary stages of research, but this paper provides background for the problem being studied and a description of our initial approach to examining the problem. In the remainder of this paper, we first describe reinforcement learning in more detail. Next, we present the game OTHELLO. Finally we argue that a restricted form of the game meets the requirements of our study, and describe our preliminary approach to finding an optimal solution to the problem.

How we learn to make decisions: rapid propagation of reinforcement learning prediction errors in humans.

PubMed

Krigolson, Olav E; Hassall, Cameron D; Handy, Todd C

2014-03-01

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

PubMed

Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Context-dependent decision-making: a simple Bayesian model

PubMed Central

Lloyd, Kevin; Leslie, David S.

2013-01-01

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or ‘contexts’ allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling scheme in which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects. PMID:23427101

Context-dependent decision-making: a simple Bayesian model.

PubMed

Lloyd, Kevin; Leslie, David S

2013-05-06

Many phenomena in animal learning can be explained by a context-learning process whereby an animal learns about different patterns of relationship between environmental variables. Differentiating between such environmental regimes or 'contexts' allows an animal to rapidly adapt its behaviour when context changes occur. The current work views animals as making sequential inferences about current context identity in a world assumed to be relatively stable but also capable of rapid switches to previously observed or entirely new contexts. We describe a novel decision-making model in which contexts are assumed to follow a Chinese restaurant process with inertia and full Bayesian inference is approximated by a sequential-sampling scheme in which only a single hypothesis about current context is maintained. Actions are selected via Thompson sampling, allowing uncertainty in parameters to drive exploration in a straightforward manner. The model is tested on simple two-alternative choice problems with switching reinforcement schedules and the results compared with rat behavioural data from a number of T-maze studies. The model successfully replicates a number of important behavioural effects: spontaneous recovery, the effect of partial reinforcement on extinction and reversal, the overtraining reversal effect, and serial reversal-learning effects.

Reinforcement learning signals in the human striatum distinguish learners from nonlearners during reward-based decision making.

PubMed

Schönberg, Tom; Daw, Nathaniel D; Joel, Daphna; O'Doherty, John P

2007-11-21

The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.

Enhanced appetitive learning and reversal learning in a mouse model for Prader-Willi syndrome.

PubMed

Relkovic, Dinko; Humby, Trevor; Hagan, Jim J; Wilkinson, Lawrence S; Isles, Anthony R

2012-06-01

Prader-Willi syndrome (PWS) is caused by lack of paternally derived gene expression from the imprinted gene cluster on human chromosome 15q11-q13. PWS is characterized by severe hypotonia, a failure to thrive in infancy and, on emerging from infancy, evidence of learning disabilities and overeating behavior due to an abnormal satiety response and increased motivation by food. We have previously shown that an imprinting center deletion mouse model (PWS-IC) is quicker to acquire a preference for, and consume more of a palatable food. Here we examined how the use of this palatable food as a reinforcer influences learning in PWS-IC mice performing a simple appetitive learning task. On a nonspatial maze-based task, PWS-IC mice acquired criteria much quicker, making fewer errors during initial acquisition and also reversal learning. A manipulation where the reinforcer was devalued impaired wild-type performance but had no effect on PWS-IC mice. This suggests that increased motivation for the reinforcer in PWS-IC mice may underlie their enhanced learning. This supports previous findings in PWS patients and is the first behavioral study of an animal model of PWS in which the motivation of behavior by food rewards has been examined. © 2012 American Psychological Association

Inquiry-Based Pre-Engineering Activities for K-4 Students

ERIC Educational Resources Information Center

Perrin, Michele

2004-01-01

This paper uses inquiry-based learning to introduce primary students to the concepts and terminology found in four introductory engineering courses: Differential Equations, Circuit Analysis, Thermodynamics, and Dynamics. Simple electronic sensors coupled with everyday objects, such as a troll doll, demonstrate and reinforce the physical principles…

Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning

PubMed Central

Dunovan, Kyle; Verstynen, Timothy

2016-01-01

The flexibility of behavioral control is a testament to the brain's capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning. PMID:27047328

Believer-Skeptic Meets Actor-Critic: Rethinking the Role of Basal Ganglia Pathways during Decision-Making and Reinforcement Learning.

PubMed

Dunovan, Kyle; Verstynen, Timothy

2016-01-01

The flexibility of behavioral control is a testament to the brain's capacity for dynamically resolving uncertainty during goal-directed actions. This ability to select actions and learn from immediate feedback is driven by the dynamics of basal ganglia (BG) pathways. A growing body of empirical evidence conflicts with the traditional view that these pathways act as independent levers for facilitating (i.e., direct pathway) or suppressing (i.e., indirect pathway) motor output, suggesting instead that they engage in a dynamic competition during action decisions that computationally captures action uncertainty. Here we discuss the utility of encoding action uncertainty as a dynamic competition between opposing control pathways and provide evidence that this simple mechanism may have powerful implications for bridging neurocomputational theories of decision making and reinforcement learning.

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

NASA Technical Reports Server (NTRS)

Bell, Ann Maria

1999-01-01

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

The Effects of Basolateral Amygdala Lesions on Unblocking

PubMed Central

Chang, Stephen E.; McDannald, Michael A.; Wheeler, Daniel S.; Holland, Peter C.

2012-01-01

Prior reinforcement of a neutral stimulus often blocks subsequent conditioning of a new stimulus if a compound of the original and new cues is paired with the same reinforcer. However, if the value of the reinforcer is altered when the compound is presented, the new cue typically acquires conditioning, a result called unblocking. Blocking, unblocking and related phenomena have been attributed to variations in processing of either the reinforcer, for example, the Rescorla-Wagner (1972) model, or cues, for example, the Pearce-Hall (1980) model. Here, we examined the effects of lesions of the basolateral amygdala on the occurrence of unblocking when the food reinforcer was increased in quantity at the time of introduction of the new cue. The lesions had no effects on unblocking in a simple design (Experiment 1), which did not distinguish between unblocking produced by variations in reward or cue processing. However, in a procedure that distinguished between unblocking due to direct conditioning by the added reinforcer, consistent with the Rescorla-Wagner (1972) model, and that due to increases in conditioning to the original reinforcer, consistent with the Pearce-Hall (1980) and other models of learning, the lesions prevented unblocking of the latter type. These results were discussed in the context of roles of the basolateral amygdala in coding and using reward prediction error information in associative learning. PMID:22448857

Working Memory Load Strengthens Reward Prediction Errors.

PubMed

Collins, Anne G E; Ciullo, Brittany; Frank, Michael J; Badre, David

2017-04-19

Reinforcement learning (RL) in simple instrumental tasks is usually modeled as a monolithic process in which reward prediction errors (RPEs) are used to update expected values of choice options. This modeling ignores the different contributions of different memory and decision-making systems thought to contribute even to simple learning. In an fMRI experiment, we investigated how working memory (WM) and incremental RL processes interact to guide human learning. WM load was manipulated by varying the number of stimuli to be learned across blocks. Behavioral results and computational modeling confirmed that learning was best explained as a mixture of two mechanisms: a fast, capacity-limited, and delay-sensitive WM process together with slower RL. Model-based analysis of fMRI data showed that striatum and lateral prefrontal cortex were sensitive to RPE, as shown previously, but, critically, these signals were reduced when the learning problem was within capacity of WM. The degree of this neural interaction related to individual differences in the use of WM to guide behavioral learning. These results indicate that the two systems do not process information independently, but rather interact during learning. SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably productive at improving our understanding of instrumental learning as well as dopaminergic and striatal network function across many mammalian species. However, this neural network is only one contributor to human learning and other mechanisms such as prefrontal cortex working memory also play a key role. Our results also show that these other players interact with the dopaminergic RL system, interfering with its key computation of reward prediction errors. Copyright © 2017 the authors 0270-6474/17/374332-11$15.00/0.

A universal role of the ventral striatum in reward-based learning: Evidence from human studies

PubMed Central

Daniel, Reka; Pollmann, Stefan

2014-01-01

Reinforcement learning enables organisms to adjust their behavior in order to maximize rewards. Electrophysiological recordings of dopaminergic midbrain neurons have shown that they code the difference between actual and predicted rewards, i.e., the reward prediction error, in many species. This error signal is conveyed to both the striatum and cortical areas and is thought to play a central role in learning to optimize behavior. However, in human daily life rewards are diverse and often only indirect feedback is available. Here we explore the range of rewards that are processed by the dopaminergic system in human participants, and examine whether it is also involved in learning in the absence of explicit rewards. While results from electrophysiological recordings in humans are sparse, evidence linking dopaminergic activity to the metabolic signal recorded from the midbrain and striatum with functional magnetic resonance imaging (fMRI) is available. Results from fMRI studies suggest that the human ventral striatum (VS) receives valuation information for a diverse set of rewarding stimuli. These range from simple primary reinforcers such as juice rewards over abstract social rewards to internally generated signals on perceived correctness, suggesting that the VS is involved in learning from trial-and-error irrespective of the specific nature of provided rewards. In addition, we summarize evidence that the VS can also be implicated when learning from observing others, and in tasks that go beyond simple stimulus-action-outcome learning, indicating that the reward system is also recruited in more complex learning tasks. PMID:24825620

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Neural correlates of strategic reasoning during competitive games.

PubMed

Seo, Hyojung; Cai, Xinying; Donahue, Christopher H; Lee, Daeyeol

2014-10-17

Although human and animal behaviors are largely shaped by reinforcement and punishment, choices in social settings are also influenced by information about the knowledge and experience of other decision-makers. During competitive games, monkeys increased their payoffs by systematically deviating from a simple heuristic learning algorithm and thereby countering the predictable exploitation by their computer opponent. Neurons in the dorsomedial prefrontal cortex (dmPFC) signaled the animal's recent choice and reward history that reflected the computer's exploitative strategy. The strength of switching signals in the dmPFC also correlated with the animal's tendency to deviate from the heuristic learning algorithm. Therefore, the dmPFC might provide control signals for overriding simple heuristic learning algorithms based on the inferred strategies of the opponent. Copyright © 2014, American Association for the Advancement of Science.

Policy improvement by a model-free Dyna architecture.

PubMed

Hwang, Kao-Shing; Lo, Chia-Yue

2013-05-01

The objective of this paper is to accelerate the process of policy improvement in reinforcement learning. The proposed Dyna-style system combines two learning schemes, one of which utilizes a temporal difference method for direct learning; the other uses relative values for indirect learning in planning between two successive direct learning cycles. Instead of establishing a complicated world model, the approach introduces a simple predictor of average rewards to actor-critic architecture in the simulation (planning) mode. The relative value of a state, defined as the accumulated differences between immediate reward and average reward, is used to steer the improvement process in the right direction. The proposed learning scheme is applied to control a pendulum system for tracking a desired trajectory to demonstrate its adaptability and robustness. Through reinforcement signals from the environment, the system takes the appropriate action to drive an unknown dynamic to track desired outputs in few learning cycles. Comparisons are made between the proposed model-free method, a connectionist adaptive heuristic critic, and an advanced method of Dyna-Q learning in the experiments of labyrinth exploration. The proposed method outperforms its counterparts in terms of elapsed time and convergence rate.

Hierarchically Organized Behavior and Its Neural Foundations: A Reinforcement Learning Perspective

ERIC Educational Resources Information Center

Botvinick, Matthew M.; Niv, Yael; Barto, Andrew C.

2009-01-01

Research on human and animal behavior has long emphasized its hierarchical structure--the divisibility of ongoing behavior into discrete tasks, which are comprised of subtask sequences, which in turn are built of simple actions. The hierarchical structure of behavior has also been of enduring interest within neuroscience, where it has been widely…

Effect of Reinforcement History on Hand Choice in an Unconstrained Reaching Task

PubMed Central

Stoloff, Rebecca H.; Taylor, Jordan A.; Xu, Jing; Ridderikhoff, Arne; Ivry, Richard B.

2011-01-01

Choosing which hand to use for an action is one of the most frequent decisions people make in everyday behavior. We developed a simple reaching task in which we vary the lateral position of a target and the participant is free to reach to it with either the right or left hand. While people exhibit a strong preference to use the hand ipsilateral to the target, there is a region of uncertainty within which hand choice varies across trials. We manipulated the reinforcement rates for the two hands, either by increasing the likelihood that a reach with the non-dominant hand would successfully intersect the target or decreasing the likelihood that a reach with the dominant hand would be successful. While participants had minimal awareness of these manipulations, we observed an increase in the use of the non-dominant hand for targets presented in the region of uncertainty. We modeled the shift in hand use using a Q-learning model of reinforcement learning. The results provided a good fit of the data and indicate that the effects of increasing and decreasing the rate of positive reinforcement are additive. These experiments emphasize the role of decision processes for effector selection, and may point to a novel approach for physical rehabilitation based on intrinsic reinforcement. PMID:21472031

Learning to reach by reinforcement learning using a receptive field based function approximation approach with continuous actions.

PubMed

Tamosiunaite, Minija; Asfour, Tamim; Wörgötter, Florentin

2009-03-01

Reinforcement learning methods can be used in robotics applications especially for specific target-oriented problems, for example the reward-based recalibration of goal directed actions. To this end still relatively large and continuous state-action spaces need to be efficiently handled. The goal of this paper is, thus, to develop a novel, rather simple method which uses reinforcement learning with function approximation in conjunction with different reward-strategies for solving such problems. For the testing of our method, we use a four degree-of-freedom reaching problem in 3D-space simulated by a two-joint robot arm system with two DOF each. Function approximation is based on 4D, overlapping kernels (receptive fields) and the state-action space contains about 10,000 of these. Different types of reward structures are being compared, for example, reward-on- touching-only against reward-on-approach. Furthermore, forbidden joint configurations are punished. A continuous action space is used. In spite of a rather large number of states and the continuous action space these reward/punishment strategies allow the system to find a good solution usually within about 20 trials. The efficiency of our method demonstrated in this test scenario suggests that it might be possible to use it on a real robot for problems where mixed rewards can be defined in situations where other types of learning might be difficult.

Reinforcement learning and episodic memory in humans and animals: an integrative framework

PubMed Central

Gershman, Samuel J.; Daw, Nathaniel D.

2018-01-01

We review the psychology and neuroscience of reinforcement learning (RL), which has witnessed significant progress in the last two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, the simplicity of these tasks misses important aspects of reinforcement learning in the real world: (i) State spaces are high-dimensional, continuous, and partially observable; this implies that (ii) data are relatively sparse: indeed precisely the same situation may never be encountered twice; and also that (iii) rewards depend on long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, these theories have largely connected with procedural and semantic memory: how knowledge about action values or world models extracted gradually from many experiences can drive choice. This misses many aspects of memory related to traces of individual events, such as episodic memory. We suggest that these two gaps are related. In particular, the computational challenges can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (i) efficiently approximate value functions over complex state spaces, (ii) learn with very little data, and (iii) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system. PMID:27618944

The Effect of Context Change on Simple Acquisition Disappears with Increased Training

ERIC Educational Resources Information Center

Leon, Samuel P.; Abad, Maria J. F.; Rosas, Juan M.

2010-01-01

The goal of this experiment was to assess the impact that experience with a task has on the context specificity of the learning that occurs. Participants performed an instrumental task within a computer game where different responses were performed in the presence of discriminative stimuli to obtain reinforcers. The number of training trials (3,…

Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise.

PubMed

Therrien, Amanda S; Wolpert, Daniel M; Bastian, Amy J

2016-01-01

Reinforcement and error-based processes are essential for motor learning, with the cerebellum thought to be required only for the error-based mechanism. Here we examined learning and retention of a reaching skill under both processes. Control subjects learned similarly from reinforcement and error-based feedback, but showed much better retention under reinforcement. To apply reinforcement to cerebellar patients, we developed a closed-loop reinforcement schedule in which task difficulty was controlled based on recent performance. This schedule produced substantial learning in cerebellar patients and controls. Cerebellar patients varied in their learning under reinforcement but fully retained what was learned. In contrast, they showed complete lack of retention in error-based learning. We developed a mechanistic model of the reinforcement task and found that learning depended on a balance between exploration variability and motor noise. While the cerebellar and control groups had similar exploration variability, the patients had greater motor noise and hence learned less. Our results suggest that cerebellar damage indirectly impairs reinforcement learning by increasing motor noise, but does not interfere with the reinforcement mechanism itself. Therefore, reinforcement can be used to learn and retain novel skills, but optimal reinforcement learning requires a balance between exploration variability and motor noise. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Effective reinforcement learning following cerebellar damage requires a balance between exploration and motor noise

PubMed Central

Therrien, Amanda S.; Wolpert, Daniel M.

2016-01-01

Abstract See Miall and Galea (doi: 10.1093/awv343 ) for a scientific commentary on this article. Reinforcement and error-based processes are essential for motor learning, with the cerebellum thought to be required only for the error-based mechanism. Here we examined learning and retention of a reaching skill under both processes. Control subjects learned similarly from reinforcement and error-based feedback, but showed much better retention under reinforcement. To apply reinforcement to cerebellar patients, we developed a closed-loop reinforcement schedule in which task difficulty was controlled based on recent performance. This schedule produced substantial learning in cerebellar patients and controls. Cerebellar patients varied in their learning under reinforcement but fully retained what was learned. In contrast, they showed complete lack of retention in error-based learning. We developed a mechanistic model of the reinforcement task and found that learning depended on a balance between exploration variability and motor noise. While the cerebellar and control groups had similar exploration variability, the patients had greater motor noise and hence learned less. Our results suggest that cerebellar damage indirectly impairs reinforcement learning by increasing motor noise, but does not interfere with the reinforcement mechanism itself. Therefore, reinforcement can be used to learn and retain novel skills, but optimal reinforcement learning requires a balance between exploration variability and motor noise. PMID:26626368

Apprenticeship Learning: Learning to Schedule from Human Experts

DTIC Science & Technology

2016-06-09

approaches to learning such models are based on Markov models, such as reinforcement learning or inverse reinforcement learning (Busoniu, Babuska, and De...via inverse reinforcement learning. In ICML. Barto, A. G., and Mahadevan, S. 2003. Recent advances in hierarchical reinforcement learning. Discrete...of tasks with temporal constraints. In Proc. AAAI, 2110–2116. Odom, P., and Natarajan, S. 2015. Active advice seeking for inverse reinforcement

Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

PubMed

Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

2012-01-01

Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

Learning to use working memory: a reinforcement learning gating model of rule acquisition in rats

PubMed Central

Lloyd, Kevin; Becker, Nadine; Jones, Matthew W.; Bogacz, Rafal

2012-01-01

Learning to form appropriate, task-relevant working memory representations is a complex process central to cognition. Gating models frame working memory as a collection of past observations and use reinforcement learning (RL) to solve the problem of when to update these observations. Investigation of how gating models relate to brain and behavior remains, however, at an early stage. The current study sought to explore the ability of simple RL gating models to replicate rule learning behavior in rats. Rats were trained in a maze-based spatial learning task that required animals to make trial-by-trial choices contingent upon their previous experience. Using an abstract version of this task, we tested the ability of two gating algorithms, one based on the Actor-Critic and the other on the State-Action-Reward-State-Action (SARSA) algorithm, to generate behavior consistent with the rats'. Both models produced rule-acquisition behavior consistent with the experimental data, though only the SARSA gating model mirrored faster learning following rule reversal. We also found that both gating models learned multiple strategies in solving the initial task, a property which highlights the multi-agent nature of such models and which is of importance in considering the neural basis of individual differences in behavior. PMID:23115551

Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning

PubMed Central

Konovalov, Arkady; Krajbich, Ian

2016-01-01

Organisms appear to learn and make decisions using different strategies known as model-free and model-based learning; the former is mere reinforcement of previously rewarded actions and the latter is a forward-looking strategy that involves evaluation of action-state transition probabilities. Prior work has used neural data to argue that both model-based and model-free learners implement a value comparison process at trial onset, but model-based learners assign more weight to forward-looking computations. Here using eye-tracking, we report evidence for a different interpretation of prior results: model-based subjects make their choices prior to trial onset. In contrast, model-free subjects tend to ignore model-based aspects of the task and instead seem to treat the decision problem as a simple comparison process between two differentially valued items, consistent with previous work on sequential-sampling models of decision making. These findings illustrate a problem with assuming that experimental subjects make their decisions at the same prescribed time. PMID:27511383

Neural computations underlying inverse reinforcement learning in the human brain

PubMed Central

Pauli, Wolfgang M; Bossaerts, Peter; O'Doherty, John

2017-01-01

In inverse reinforcement learning an observer infers the reward distribution available for actions in the environment solely through observing the actions implemented by another agent. To address whether this computational process is implemented in the human brain, participants underwent fMRI while learning about slot machines yielding hidden preferred and non-preferred food outcomes with varying probabilities, through observing the repeated slot choices of agents with similar and dissimilar food preferences. Using formal model comparison, we found that participants implemented inverse RL as opposed to a simple imitation strategy, in which the actions of the other agent are copied instead of inferring the underlying reward structure of the decision problem. Our computational fMRI analysis revealed that anterior dorsomedial prefrontal cortex encoded inferences about action-values within the value space of the agent as opposed to that of the observer, demonstrating that inverse RL is an abstract cognitive process divorceable from the values and concerns of the observer him/herself. PMID:29083301

Curiosity driven reinforcement learning for motion planning on humanoids

PubMed Central

Frank, Mikhail; Leitner, Jürgen; Stollenga, Marijn; Förster, Alexander; Schmidhuber, Jürgen

2014-01-01

Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment. PMID:24432001

Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.

PubMed

Morimura, Tetsuro; Uchibe, Eiji; Yoshimoto, Junichiro; Peters, Jan; Doya, Kenji

2010-02-01

Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distribution to changes in the policy parameter. Although the bias introduced by this omission can be reduced by setting the forgetting rate gamma for the value functions close to 1, these algorithms do not permit gamma to be set exactly at gamma = 1. In this article, we propose a method for estimating the log stationary state distribution derivative (LSD) as a useful form of the derivative of the stationary state distribution through backward Markov chain formulation and a temporal difference learning framework. A new policy gradient (PG) framework with an LSD is also proposed, in which the average reward gradient can be estimated by setting gamma = 0, so it becomes unnecessary to learn the value functions. We also test the performance of the proposed algorithms using simple benchmark tasks and show that these can improve the performances of existing PG methods.

Generating Adaptive Behaviour within a Memory-Prediction Framework

PubMed Central

Rawlinson, David; Kowadlo, Gideon

2012-01-01

The Memory-Prediction Framework (MPF) and its Hierarchical-Temporal Memory implementation (HTM) have been widely applied to unsupervised learning problems, for both classification and prediction. To date, there has been no attempt to incorporate MPF/HTM in reinforcement learning or other adaptive systems; that is, to use knowledge embodied within the hierarchy to control a system, or to generate behaviour for an agent. This problem is interesting because the human neocortex is believed to play a vital role in the generation of behaviour, and the MPF is a model of the human neocortex. We propose some simple and biologically-plausible enhancements to the Memory-Prediction Framework. These cause it to explore and interact with an external world, while trying to maximize a continuous, time-varying reward function. All behaviour is generated and controlled within the MPF hierarchy. The hierarchy develops from a random initial configuration by interaction with the world and reinforcement learning only. Among other demonstrations, we show that a 2-node hierarchy can learn to successfully play “rocks, paper, scissors” against a predictable opponent. PMID:22272231

Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework.

PubMed

Gershman, Samuel J; Daw, Nathaniel D

2017-01-03

We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. However, one challenge in the study of RL is computational: The simplicity of these tasks ignores important aspects of reinforcement learning in the real world: (a) State spaces are high-dimensional, continuous, and partially observable; this implies that (b) data are relatively sparse and, indeed, precisely the same situation may never be encountered twice; furthermore, (c) rewards depend on the long-term consequences of actions in ways that violate the classical assumptions that make RL tractable. A seemingly distinct challenge is that, cognitively, theories of RL have largely involved procedural and semantic memory, the way in which knowledge about action values or world models extracted gradually from many experiences can drive choice. This focus on semantic memory leaves out many aspects of memory, such as episodic memory, related to the traces of individual events. We suggest that these two challenges are related. The computational challenge can be dealt with, in part, by endowing RL systems with episodic memory, allowing them to (a) efficiently approximate value functions over complex state spaces, (b) learn with very little data, and (c) bridge long-term dependencies between actions and rewards. We review the computational theory underlying this proposal and the empirical evidence to support it. Our proposal suggests that the ubiquitous and diverse roles of memory in RL may function as part of an integrated learning system.

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

PubMed

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

2007-01-01

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

Simple and conditional visual discrimination with wheel running as reinforcement in rats.

PubMed

Iversen, I H

1998-09-01

Three experiments explored whether access to wheel running is sufficient as reinforcement to establish and maintain simple and conditional visual discriminations in nondeprived rats. In Experiment 1, 2 rats learned to press a lit key to produce access to running; responding was virtually absent when the key was dark, but latencies to respond were longer than for customary food and water reinforcers. Increases in the intertrial interval did not improve the discrimination performance. In Experiment 2, 3 rats acquired a go-left/go-right discrimination with a trial-initiating response and reached an accuracy that exceeded 80%; when two keys showed a steady light, pressing the left key produced access to running whereas pressing the right key produced access to running when both keys showed blinking light. Latencies to respond to the lights shortened when the trial-initiation response was introduced and became much shorter than in Experiment 1. In Experiment 3, 1 rat acquired a conditional discrimination task (matching to sample) with steady versus blinking lights at an accuracy exceeding 80%. A trial-initiation response allowed self-paced trials as in Experiment 2. When the rat was exposed to the task for 19 successive 24-hr periods with access to food and water, the discrimination performance settled in a typical circadian pattern and peak accuracy exceeded 90%. When the trial-initiation response was under extinction, without access to running, the circadian activity pattern determined the time of spontaneous recovery. The experiments demonstrate that wheel-running reinforcement can be used to establish and maintain simple and conditional visual discriminations in nondeprived rats.

Post-Learning Infusion of Anisomycin into the Anterior Cingulate Cortex Impairs Instrumental Acquisition through an Effect on Reinforcer Valuation

ERIC Educational Resources Information Center

Jonkman, Sietse; Everitt, Barry J.

2009-01-01

The integrity of the rodent anterior cingulate cortex (ACC) is essential for various aspects of instrumental behavior, but it is not clear if the ACC is important for the acquisition of a simple instrumental response. Here, it was demonstrated that post-session infusions of anisomycin into the rat ACC completely prevented the acquisition of…

A Flexible Mechanism of Rule Selection Enables Rapid Feature-Based Reinforcement Learning

PubMed Central

Balcarras, Matthew; Womelsdorf, Thilo

2016-01-01

Learning in a new environment is influenced by prior learning and experience. Correctly applying a rule that maps a context to stimuli, actions, and outcomes enables faster learning and better outcomes compared to relying on strategies for learning that are ignorant of task structure. However, it is often difficult to know when and how to apply learned rules in new contexts. In our study we explored how subjects employ different strategies for learning the relationship between stimulus features and positive outcomes in a probabilistic task context. We test the hypothesis that task naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type and restricts selections to that dimension. To test this hypothesis we designed a decision making task where subjects receive probabilistic feedback following choices between pairs of stimuli. In the task, trials are grouped in two contexts by blocks, where in one type of block there is no unique relationship between a specific feature dimension (stimulus shape or color) and positive outcomes, and following an un-cued transition, alternating blocks have outcomes that are linked to either stimulus shape or color. Two-thirds of subjects (n = 22/32) exhibited behavior that was best fit by a hierarchical feature-rule model. Supporting the prediction of the model mechanism these subjects showed significantly enhanced performance in feature-reward blocks, and rapidly switched their choice strategy to using abstract feature rules when reward contingencies changed. Choice behavior of other subjects (n = 10/32) was fit by a range of alternative reinforcement learning models representing strategies that do not benefit from applying previously learned rules. In summary, these results show that untrained subjects are capable of flexibly shifting between behavioral rules by leveraging simple model-free reinforcement learning and context-specific selections to drive responses. PMID:27064794

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

Rational and Mechanistic Perspectives on Reinforcement Learning

ERIC Educational Resources Information Center

Chater, Nick

2009-01-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: "mechanistic" and "rational." Reinforcement learning is often viewed in mechanistic terms--as…

Learned helplessness and learned prevalence: exploring the causal relations among perceived controllability, reward prevalence, and exploration.

PubMed

Teodorescu, Kinneret; Erev, Ido

2014-10-01

Exposure to uncontrollable outcomes has been found to trigger learned helplessness, a state in which the agent, because of lack of exploration, fails to take advantage of regained control. Although the implications of this phenomenon have been widely studied, its underlying cause remains undetermined. One can learn not to explore because the environment is uncontrollable, because the average reinforcement for exploring is low, or because rewards for exploring are rare. In the current research, we tested a simple experimental paradigm that contrasts the predictions of these three contributors and offers a unified psychological mechanism that underlies the observed phenomena. Our results demonstrate that learned helplessness is not correlated with either the perceived controllability of one's environment or the average reward, which suggests that reward prevalence is a better predictor of exploratory behavior than the other two factors. A simple computational model in which exploration decisions were based on small samples of past experiences captured the empirical phenomena while also providing a cognitive basis for feelings of uncontrollability. © The Author(s) 2014.

Dynamic Response-by-Response Models of Matching Behavior in Rhesus Monkeys

PubMed Central

Lau, Brian; Glimcher, Paul W

2005-01-01

We studied the choice behavior of 2 monkeys in a discrete-trial task with reinforcement contingencies similar to those Herrnstein (1961) used when he described the matching law. In each session, the monkeys experienced blocks of discrete trials at different relative-reinforcer frequencies or magnitudes with unsignalled transitions between the blocks. Steady-state data following adjustment to each transition were well characterized by the generalized matching law; response ratios undermatched reinforcer frequency ratios but matched reinforcer magnitude ratios. We modelled response-by-response behavior with linear models that used past reinforcers as well as past choices to predict the monkeys' choices on each trial. We found that more recently obtained reinforcers more strongly influenced choice behavior. Perhaps surprisingly, we also found that the monkeys' actions were influenced by the pattern of their own past choices. It was necessary to incorporate both past reinforcers and past choices in order to accurately capture steady-state behavior as well as the fluctuations during block transitions and the response-by-response patterns of behavior. Our results suggest that simple reinforcement learning models must account for the effects of past choices to accurately characterize behavior in this task, and that models with these properties provide a conceptual tool for studying how both past reinforcers and past choices are integrated by the neural systems that generate behavior. PMID:16596980

Negative reinforcement learning is affected in substance dependence.

PubMed

Thompson, Laetitia L; Claus, Eric D; Mikulich-Gilbertson, Susan K; Banich, Marie T; Crowley, Thomas; Krmpotich, Theodore; Miller, David; Tanabe, Jody

2012-06-01

Negative reinforcement results in behavior to escape or avoid an aversive outcome. Withdrawal symptoms are purported to be negative reinforcers in perpetuating substance dependence, but little is known about negative reinforcement learning in this population. The purpose of this study was to examine reinforcement learning in substance dependent individuals (SDI), with an emphasis on assessing negative reinforcement learning. We modified the Iowa Gambling Task to separately assess positive and negative reinforcement. We hypothesized that SDI would show differences in negative reinforcement learning compared to controls and we investigated whether learning differed as a function of the relative magnitude or frequency of the reinforcer. Thirty subjects dependent on psychostimulants were compared with 28 community controls on a decision making task that manipulated outcome frequencies and magnitudes and required an action to avoid a negative outcome. SDI did not learn to avoid negative outcomes to the same degree as controls. This difference was driven by the magnitude, not the frequency, of negative feedback. In contrast, approach behaviors in response to positive reinforcement were similar in both groups. Our findings are consistent with a specific deficit in negative reinforcement learning in SDI. SDI were relatively insensitive to the magnitude, not frequency, of loss. If this generalizes to drug-related stimuli, it suggests that repeated episodes of withdrawal may drive relapse more than the severity of a single episode. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

An Upside to Reward Sensitivity: The Hippocampus Supports Enhanced Reinforcement Learning in Adolescence.

PubMed

Davidow, Juliet Y; Foerde, Karin; Galván, Adriana; Shohamy, Daphna

2016-10-05

Adolescents are notorious for engaging in reward-seeking behaviors, a tendency attributed to heightened activity in the brain's reward systems during adolescence. It has been suggested that reward sensitivity in adolescence might be adaptive, but evidence of an adaptive role has been scarce. Using a probabilistic reinforcement learning task combined with reinforcement learning models and fMRI, we found that adolescents showed better reinforcement learning and a stronger link between reinforcement learning and episodic memory for rewarding outcomes. This behavioral benefit was related to heightened prediction error-related BOLD activity in the hippocampus and to stronger functional connectivity between the hippocampus and the striatum at the time of reinforcement. These findings reveal an important role for the hippocampus in reinforcement learning in adolescence and suggest that reward sensitivity in adolescence is related to adaptive differences in how adolescents learn from experience. Copyright © 2016 Elsevier Inc. All rights reserved.

Aversive Learning Modulates Cortical Representations of Object Categories

PubMed Central

Dunsmoor, Joseph E.; Kragel, Philip A.; Martin, Alex; LaBar, Kevin S.

2014-01-01

Experimental studies of conditioned learning reveal activity changes in the amygdala and unimodal sensory cortex underlying fear acquisition to simple stimuli. However, real-world fears typically involve complex stimuli represented at the category level. A consequence of category-level representations of threat is that aversive experiences with particular category members may lead one to infer that related exemplars likewise pose a threat, despite variations in physical form. Here, we examined the effect of category-level representations of threat on human brain activation using 2 superordinate categories (animals and tools) as conditioned stimuli. Hemodynamic activity in the amygdala and category-selective cortex was modulated by the reinforcement contingency, leading to widespread fear of different exemplars from the reinforced category. Multivariate representational similarity analyses revealed that activity patterns in the amygdala and object-selective cortex were more similar among exemplars from the threat versus safe category. Learning to fear animate objects was additionally characterized by enhanced functional coupling between the amygdala and fusiform gyrus. Finally, hippocampal activity co-varied with object typicality and amygdala activation early during training. These findings provide novel evidence that aversive learning can modulate category-level representations of object concepts, thereby enabling individuals to express fear to a range of related stimuli. PMID:23709642

An automated device for appetitive conditioning in zebrafish (Danio rerio).

PubMed

Manabe, Kazuchika; Dooling, R J; Takaku, Shinichi

2013-12-01

An automated device and a procedure for the operant conditioning individual zebrafish were developed. The key feature of this procedure was the construction of a simple, inexpensive feeder that can deliver extremely small amounts of food, thus preventing rapid satiation. This allows the experimenter to run multiple trails in a single test session and multiple sessions in one day. In addition, small response keys made from acryl rods and fiber sensors were developed that were sufficiently sensitive to detect fish contact. To illustrate the efficiency and utility of the device for traditional learning paradigms, we trained zebrafish in a fixed ratio schedule where subjects were reinforced with food after 10 responses. Zebrafish reliably responded on the response key for sessions that lasted as long 80-reinforcements. They also showed the traditional "break and run" response pattern that has been found in many species. These results show that this system will be valuable for behavioral studies with zebrafish, especially for experiments that need many repeated trials using food reinforcer in a session. The present system can be used for sensory and learning investigations, as well applications in behavioral pharmacology, behavioral genetics, and toxicology where the zebrafish is becoming the vertebrate model of choice.

Reinforcement learning for stabilizing an inverted pendulum naturally leads to intermittent feedback control as in human quiet standing.

PubMed

Michimoto, Kenjiro; Suzuki, Yasuyuki; Kiyono, Ken; Kobayashi, Yasushi; Morasso, Pietro; Nomura, Taishin

2016-08-01

Intermittent feedback control for stabilizing human upright stance is a promising strategy, alternative to the standard time-continuous stiffness control. Here we show that such an intermittent controller can be established naturally through reinforcement learning. To this end, we used a single inverted pendulum model of the upright posture and a very simple reward function that gives a certain amount of punishments when the inverted pendulum falls or changes its position in the state space. We found that the acquired feedback controller exhibits hallmarks of the intermittent feedback control strategy, namely the action of the feedback controller is switched-off intermittently when the state of the pendulum is located near the stable manifold of the unstable saddle-type upright equilibrium of the inverted pendulum with no active control: this action provides an opportunity to exploit transiently converging dynamics toward the unstable upright position with no help of the active feedback control. We then speculate about a possible physiological mechanism of such reinforcement learning, and suggest that it may be related to the neural activity in the pedunculopontine tegmental nucleus (PPN) of the brainstem. This hypothesis is supported by recent evidence indicating that PPN might play critical roles for generation and regulation of postural tonus, reward prediction, as well as postural instability in patients with Parkinson's disease.

Stimulus Processing and Associative Learning in Wistar and WKHA Rats

PubMed Central

Chess, Amy C.; Keene, Christopher S.; Wyzik, Elizabeth C.; Bucci, David J.

2007-01-01

This study assessed basic learning and attention abilities in WKHA (Wistar-Kyoto Hyperactive) rats using appetitive conditioning preparations. Two measures of conditioned responding to a visual stimulus, orienting behavior (rearing on the hindlegs) and food cup behavior (placing the head inside the recessed food cup) were measured. In Experiment 1, simple conditioning but not extinction was impaired in WKHA rats compared to Wistar rats. In Experiment 2, non-reinforced presentations of the visual cue preceded the conditioning sessions. WKHA rats displayed less orienting behavior than Wistar rats, but comparable levels of food cup behavior. These data suggest that WKHA rats exhibit specific abnormalities in attentional processing as well as learning stimulus-reward relationships. PMID:15998198

Roles of OA1 octopamine receptor and Dop1 dopamine receptor in mediating appetitive and aversive reinforcement revealed by RNAi studies

PubMed Central

Awata, Hiroko; Wakuda, Ryo; Ishimaru, Yoshiyasu; Matsuoka, Yuji; Terao, Kanta; Katata, Satomi; Matsumoto, Yukihisa; Hamanaka, Yoshitaka; Noji, Sumihare; Mito, Taro; Mizunami, Makoto

2016-01-01

Revealing reinforcing mechanisms in associative learning is important for elucidation of brain mechanisms of behavior. In mammals, dopamine neurons are thought to mediate both appetitive and aversive reinforcement signals. Studies using transgenic fruit-flies suggested that dopamine neurons mediate both appetitive and aversive reinforcements, through the Dop1 dopamine receptor, but our studies using octopamine and dopamine receptor antagonists and using Dop1 knockout crickets suggested that octopamine neurons mediate appetitive reinforcement and dopamine neurons mediate aversive reinforcement in associative learning in crickets. To fully resolve this issue, we examined the effects of silencing of expression of genes that code the OA1 octopamine receptor and Dop1 and Dop2 dopamine receptors by RNAi in crickets. OA1-silenced crickets exhibited impairment in appetitive learning with water but not in aversive learning with sodium chloride solution, while Dop1-silenced crickets exhibited impairment in aversive learning but not in appetitive learning. Dop2-silenced crickets showed normal scores in both appetitive learning and aversive learning. The results indicate that octopamine neurons mediate appetitive reinforcement via OA1 and that dopamine neurons mediate aversive reinforcement via Dop1 in crickets, providing decisive evidence that neurotransmitters and receptors that mediate appetitive reinforcement indeed differ among different species of insects. PMID:27412401

Roles of OA1 octopamine receptor and Dop1 dopamine receptor in mediating appetitive and aversive reinforcement revealed by RNAi studies.

PubMed

Awata, Hiroko; Wakuda, Ryo; Ishimaru, Yoshiyasu; Matsuoka, Yuji; Terao, Kanta; Katata, Satomi; Matsumoto, Yukihisa; Hamanaka, Yoshitaka; Noji, Sumihare; Mito, Taro; Mizunami, Makoto

2016-07-14

Revealing reinforcing mechanisms in associative learning is important for elucidation of brain mechanisms of behavior. In mammals, dopamine neurons are thought to mediate both appetitive and aversive reinforcement signals. Studies using transgenic fruit-flies suggested that dopamine neurons mediate both appetitive and aversive reinforcements, through the Dop1 dopamine receptor, but our studies using octopamine and dopamine receptor antagonists and using Dop1 knockout crickets suggested that octopamine neurons mediate appetitive reinforcement and dopamine neurons mediate aversive reinforcement in associative learning in crickets. To fully resolve this issue, we examined the effects of silencing of expression of genes that code the OA1 octopamine receptor and Dop1 and Dop2 dopamine receptors by RNAi in crickets. OA1-silenced crickets exhibited impairment in appetitive learning with water but not in aversive learning with sodium chloride solution, while Dop1-silenced crickets exhibited impairment in aversive learning but not in appetitive learning. Dop2-silenced crickets showed normal scores in both appetitive learning and aversive learning. The results indicate that octopamine neurons mediate appetitive reinforcement via OA1 and that dopamine neurons mediate aversive reinforcement via Dop1 in crickets, providing decisive evidence that neurotransmitters and receptors that mediate appetitive reinforcement indeed differ among different species of insects.

Longitudinal investigation on learned helplessness tested under negative and positive reinforcement involving stimulus control.

PubMed

Oliveira, Emileane C; Hunziker, Maria Helena

2014-07-01

In this study, we investigated whether (a) animals demonstrating the learned helplessness effect during an escape contingency also show learning deficits under positive reinforcement contingencies involving stimulus control and (b) the exposure to positive reinforcement contingencies eliminates the learned helplessness effect under an escape contingency. Rats were initially exposed to controllable (C), uncontrollable (U) or no (N) shocks. After 24h, they were exposed to 60 escapable shocks delivered in a shuttlebox. In the following phase, we selected from each group the four subjects that presented the most typical group pattern: no escape learning (learned helplessness effect) in Group U and escape learning in Groups C and N. All subjects were then exposed to two phases, the (1) positive reinforcement for lever pressing under a multiple FR/Extinction schedule and (2) a re-test under negative reinforcement (escape). A fourth group (n=4) was exposed only to the positive reinforcement sessions. All subjects showed discrimination learning under multiple schedule. In the escape re-test, the learned helplessness effect was maintained for three of the animals in Group U. These results suggest that the learned helplessness effect did not extend to discriminative behavior that is positively reinforced and that the learned helplessness effect did not revert for most subjects after exposure to positive reinforcement. We discuss some theoretical implications as related to learned helplessness as an effect restricted to aversive contingencies and to the absence of reversion after positive reinforcement. This article is part of a Special Issue entitled: insert SI title. Copyright © 2014. Published by Elsevier B.V.

Rational and mechanistic perspectives on reinforcement learning.

PubMed

Chater, Nick

2009-12-01

This special issue describes important recent developments in applying reinforcement learning models to capture neural and cognitive function. But reinforcement learning, as a theoretical framework, can apply at two very different levels of description: mechanistic and rational. Reinforcement learning is often viewed in mechanistic terms--as describing the operation of aspects of an agent's cognitive and neural machinery. Yet it can also be viewed as a rational level of description, specifically, as describing a class of methods for learning from experience, using minimal background knowledge. This paper considers how rational and mechanistic perspectives differ, and what types of evidence distinguish between them. Reinforcement learning research in the cognitive and brain sciences is often implicitly committed to the mechanistic interpretation. Here the opposite view is put forward: that accounts of reinforcement learning should apply at the rational level, unless there is strong evidence for a mechanistic interpretation. Implications of this viewpoint for reinforcement-based theories in the cognitive and brain sciences are discussed.

Stress affects instrumental learning based on positive or negative reinforcement in interaction with personality in domestic horses

PubMed Central

Valenchon, Mathilde; Lévy, Frédéric; Moussu, Chantal; Lansade, Léa

2017-01-01

The present study investigated how stress affects instrumental learning performance in horses (Equus caballus) depending on the type of reinforcement. Horses were assigned to four groups (N = 15 per group); each group received training with negative or positive reinforcement in the presence or absence of stressors unrelated to the learning task. The instrumental learning task consisted of the horse entering one of two compartments at the appearance of a visual signal given by the experimenter. In the absence of stressors unrelated to the task, learning performance did not differ between negative and positive reinforcements. The presence of stressors unrelated to the task (exposure to novel and sudden stimuli) impaired learning performance. Interestingly, this learning deficit was smaller when the negative reinforcement was used. The negative reinforcement, considered as a stressor related to the task, could have counterbalanced the impact of the extrinsic stressor by focusing attention toward the learning task. In addition, learning performance appears to differ between certain dimensions of personality depending on the presence of stressors and the type of reinforcement. These results suggest that when negative reinforcement is used (i.e. stressor related to the task), the most fearful horses may be the best performers in the absence of stressors but the worst performers when stressors are present. On the contrary, when positive reinforcement is used, the most fearful horses appear to be consistently the worst performers, with and without exposure to stressors unrelated to the learning task. This study is the first to demonstrate in ungulates that stress affects learning performance differentially according to the type of reinforcement and in interaction with personality. It provides fundamental and applied perspectives in the understanding of the relationships between personality and training abilities. PMID:28475581

Racial bias shapes social reinforcement learning.

PubMed

Lindström, Björn; Selbing, Ida; Molapour, Tanaz; Olsson, Andreas

2014-03-01

Both emotional facial expressions and markers of racial-group belonging are ubiquitous signals in social interaction, but little is known about how these signals together affect future behavior through learning. To address this issue, we investigated how emotional (threatening or friendly) in-group and out-group faces reinforced behavior in a reinforcement-learning task. We asked whether reinforcement learning would be modulated by intergroup attitudes (i.e., racial bias). The results showed that individual differences in racial bias critically modulated reinforcement learning. As predicted, racial bias was associated with more efficiently learned avoidance of threatening out-group individuals. We used computational modeling analysis to quantitatively delimit the underlying processes affected by social reinforcement. These analyses showed that racial bias modulates the rate at which exposure to threatening out-group individuals is transformed into future avoidance behavior. In concert, these results shed new light on the learning processes underlying social interaction with racial-in-group and out-group individuals.

"Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

PubMed

Liu, Chunming; Xu, Xin; Hu, Dewen

2013-04-29

Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

NASA Astrophysics Data System (ADS)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

GA-based fuzzy reinforcement learning for control of a magnetic bearing system.

PubMed

Lin, C T; Jou, C P

2000-01-01

This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.

Contextual modulation of value signals in reward and punishment learning

PubMed Central

Palminteri, Stefano; Khamassi, Mehdi; Joffily, Mateus; Coricelli, Giorgio

2015-01-01

Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative—context-dependent—scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system. PMID:26302782

Contextual modulation of value signals in reward and punishment learning.

PubMed

Palminteri, Stefano; Khamassi, Mehdi; Joffily, Mateus; Coricelli, Giorgio

2015-08-25

Compared with reward seeking, punishment avoidance learning is less clearly understood at both the computational and neurobiological levels. Here we demonstrate, using computational modelling and fMRI in humans, that learning option values in a relative--context-dependent--scale offers a simple computational solution for avoidance learning. The context (or state) value sets the reference point to which an outcome should be compared before updating the option value. Consequently, in contexts with an overall negative expected value, successful punishment avoidance acquires a positive value, thus reinforcing the response. As revealed by post-learning assessment of options values, contextual influences are enhanced when subjects are informed about the result of the forgone alternative (counterfactual information). This is mirrored at the neural level by a shift in negative outcome encoding from the anterior insula to the ventral striatum, suggesting that value contextualization also limits the need to mobilize an opponent punishment learning system.

11.2 YIP Human In the Loop Statistical RelationalLearners

DTIC Science & Technology

2017-10-23

learning formalisms including inverse reinforcement learning [4] and statistical relational learning [7, 5, 8]. We have also applied our algorithms in...one introduced for label preferences. 4 Figure 2: Active Advice Seeking for Inverse Reinforcement Learning. active advice seeking is in selecting the...learning tasks. 1.2.1 Sequential Decision-Making Our previous work on advice for inverse reinforcement learning (IRL) defined advice as action

Effects of dopamine on reinforcement learning and consolidation in Parkinson's disease.

PubMed

Grogan, John P; Tsivos, Demitra; Smith, Laura; Knight, Brogan E; Bogacz, Rafal; Whone, Alan; Coulthard, Elizabeth J

2017-07-10

Emerging evidence suggests that dopamine may modulate learning and memory with important implications for understanding the neurobiology of memory and future therapeutic targeting. An influential hypothesis posits that dopamine biases reinforcement learning. More recent data also suggest an influence during both consolidation and retrieval. Eighteen Parkinson's disease patients learned through feedback ON or OFF medication, with memory tested 24 hr later ON or OFF medication (4 conditions, within-subjects design with matched healthy control group). Patients OFF medication during learning decreased in memory accuracy over the following 24 hr. In contrast to previous studies, however, dopaminergic medication during learning and testing did not affect expression of positive or negative reinforcement. Two further experiments were run without the 24 hr delay, but they too failed to reproduce effects of dopaminergic medication on reinforcement learning. While supportive of a dopaminergic role in consolidation, this study failed to replicate previous findings on reinforcement learning.

Toward cognitive robotics

NASA Astrophysics Data System (ADS)

Laird, John E.

2009-05-01

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledge - knowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.

Enhanced Experience Replay for Deep Reinforcement Learning

DTIC Science & Technology

2015-11-01

ARL-TR-7538 ● NOV 2015 US Army Research Laboratory Enhanced Experience Replay for Deep Reinforcement Learning by David Doria...Experience Replay for Deep Reinforcement Learning by David Doria, Bryan Dawson, and Manuel Vindiola Computational and Information Sciences Directorate...

Pavlovian, Skinner, and Other Behaviourists' Contributions to AI. Chapter 9

NASA Technical Reports Server (NTRS)

Kosinski, Withold; Zaczek-Chrzanowska, Dominika

2007-01-01

A version of the definition of intelligent behaviour will be supplied in the context of real and artificial systems. Short presentation of principles of learning, starting with Pavlovian s classical conditioning through reinforced response and operant conditioning of Thorndike and Skinner and finishing with cognitive learning of Tolman and Bandura will be given. The most important figures within behaviourism, especially those with contribution to AI, will be described. Some tools of artificial intelligence that act according to those principles will be presented. An attempt will be made to show when some simple rules for behaviour modifications can lead to a complex intelligent behaviour.

Prespeech motor learning in a neural network using reinforcement.

PubMed

Warlaumont, Anne S; Westermann, Gert; Buder, Eugene H; Oller, D Kimbrough

2013-02-01

Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one's language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the different conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network's post-learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network's post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model's post-learning productions were more likely to resemble the English vowels and vice versa. Copyright © 2012 Elsevier Ltd. All rights reserved.

Renewal after the punishment of free operant behavior.

PubMed

Bouton, Mark E; Schepers, Scott T

2015-01-01

Three experiments examined the role of context in punishment learning. In Experiment 1, rats were trained to lever press for food in Context A and then punished for responding in Context B (by presenting response-contingent footshock). Punishment led to complete suppression of the response. However, when responding was tested (in extinction) in Contexts A and B, a strong renewal of responding occurred in Context A. In Experiment 2, renewal also occurred when initial reinforcement occurred in Context A, punishment occurred in Context B, and testing occurred in a new context (Context C). In both experiments, behavioral suppression and renewal were not observed in groups that received noncontingent (yoked) footshocks in Context B. In Experiment 3, 2 responses (lever press and chain pull) were separately reinforced in Contexts A and B and then punished in the opposite context. Although the procedure equated the contexts on their association with reinforcement and punishment, renewal of each response was observed when it was tested in its nonpunished context. The contexts also influenced response choice. Overall, the results suggest that punishment is specific to the context in which it is learned, and establish that its context-specificity does not depend on a simple association between the context and shock. Like extinction, punishment may involve learning to inhibit a specific response in a specific context. Implications for theories of punishment and for understanding the cessation of problematic operant behavior (e.g., drug abuse) are discussed. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Reconciling Reinforcement Learning Models with Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

ERIC Educational Resources Information Center

Redish, A. David; Jensen, Steve; Johnson, Adam; Kurth-Nelson, Zeb

2007-01-01

Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL…

Behavioral and neural properties of social reinforcement learning

PubMed Central

Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Libby, Victoria; Glover, Gary; Voss, Henning U.; Ballon, Douglas J.; Casey, BJ

2011-01-01

Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based upon work in non-human primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging (fMRI). Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis - social preferences, response latencies and modeling neural responses – are consistent with reinforcement learning theory and non-human primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one’s peers in altering subsequent behavior. PMID:21917787

The combination of appetitive and aversive reinforcers and the nature of their interaction during auditory learning.

PubMed

Ilango, A; Wetzel, W; Scheich, H; Ohl, F W

2010-03-31

Learned changes in behavior can be elicited by either appetitive or aversive reinforcers. It is, however, not clear whether the two types of motivation, (approaching appetitive stimuli and avoiding aversive stimuli) drive learning in the same or different ways, nor is their interaction understood in situations where the two types are combined in a single experiment. To investigate this question we have developed a novel learning paradigm for Mongolian gerbils, which not only allows rewards and punishments to be presented in isolation or in combination with each other, but also can use these opposite reinforcers to drive the same learned behavior. Specifically, we studied learning of tone-conditioned hurdle crossing in a shuttle box driven by either an appetitive reinforcer (brain stimulation reward) or an aversive reinforcer (electrical footshock), or by a combination of both. Combination of the two reinforcers potentiated speed of acquisition, led to maximum possible performance, and delayed extinction as compared to either reinforcer alone. Additional experiments, using partial reinforcement protocols and experiments in which one of the reinforcers was omitted after the animals had been previously trained with the combination of both reinforcers, indicated that appetitive and aversive reinforcers operated together but acted in different ways: in this particular experimental context, punishment appeared to be more effective for initial acquisition and reward more effective to maintain a high level of conditioned responses (CRs). The results imply that learning mechanisms in problem solving were maximally effective when the initial punishment of mistakes was combined with the subsequent rewarding of correct performance. Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.

Punishment Insensitivity and Impaired Reinforcement Learning in Preschoolers

ERIC Educational Resources Information Center

Briggs-Gowan, Margaret J.; Nichols, Sara R.; Voss, Joel; Zobel, Elvira; Carter, Alice S.; McCarthy, Kimberly J.; Pine, Daniel S.; Blair, James; Wakschlag, Lauren S.

2014-01-01

Background: Youth and adults with psychopathic traits display disrupted reinforcement learning. Advances in measurement now enable examination of this association in preschoolers. The current study examines relations between reinforcement learning in preschoolers and parent ratings of reduced responsiveness to socialization, conceptualized as a…

The cerebellum: a neural system for the study of reinforcement learning.

PubMed

Swain, Rodney A; Kerr, Abigail L; Thompson, Richard F

2011-01-01

In its strictest application, the term "reinforcement learning" refers to a computational approach to learning in which an agent (often a machine) interacts with a mutable environment to maximize reward through trial and error. The approach borrows essentials from several fields, most notably Computer Science, Behavioral Neuroscience, and Psychology. At the most basic level, a neural system capable of mediating reinforcement learning must be able to acquire sensory information about the external environment and internal milieu (either directly or through connectivities with other brain regions), must be able to select a behavior to be executed, and must be capable of providing evaluative feedback about the success of that behavior. Given that Psychology informs us that reinforcers, both positive and negative, are stimuli or consequences that increase the probability that the immediately antecedent behavior will be repeated and that reinforcer strength or viability is modulated by the organism's past experience with the reinforcer, its affect, and even the state of its muscles (e.g., eyes open or closed); it is the case that any neural system that supports reinforcement learning must also be sensitive to these same considerations. Once learning is established, such a neural system must finally be able to maintain continued response expression and prevent response drift. In this report, we examine both historical and recent evidence that the cerebellum satisfies all of these requirements. While we report evidence from a variety of learning paradigms, the majority of our discussion will focus on classical conditioning of the rabbit eye blink response as an ideal model system for the study of reinforcement and reinforcement learning.

The role of GABAB receptors in human reinforcement learning.

PubMed

Ort, Andres; Kometer, Michael; Rohde, Judith; Seifritz, Erich; Vollenweider, Franz X

2014-10-01

Behavioral evidence from human studies suggests that the γ-aminobutyric acid type B receptor (GABAB receptor) agonist baclofen modulates reinforcement learning and reduces craving in patients with addiction spectrum disorders. However, in contrast to the well established role of dopamine in reinforcement learning, the mechanisms by which the GABAB receptor influences reinforcement learning in humans remain completely unknown. To further elucidate this issue, a cross-over, double-blind, placebo-controlled study was performed in healthy human subjects (N=15) to test the effects of baclofen (20 and 50mg p.o.) on probabilistic reinforcement learning. Outcomes were the feedback-induced P2 component of the event-related potential, the feedback-related negativity, and the P300 component of the event-related potential. Baclofen produced a reduction of P2 amplitude over the course of the experiment, but did not modulate the feedback-related negativity. Furthermore, there was a trend towards increased learning after baclofen administration relative to placebo over the course of the experiment. The present results extend previous theories of reinforcement learning, which focus on the importance of mesolimbic dopamine signaling, and indicate that stimulation of cortical GABAB receptors in a fronto-parietal network leads to better attentional allocation in reinforcement learning. This observation is a first step in our understanding of how baclofen may improve reinforcement learning in healthy subjects. Further studies with bigger sample sizes are needed to corroborate this conclusion and furthermore, test this effect in patients with addiction spectrum disorder. Copyright © 2014 Elsevier B.V. and ECNP. All rights reserved.

Effects of dopamine on reinforcement learning and consolidation in Parkinson’s disease

PubMed Central

Grogan, John P; Tsivos, Demitra; Smith, Laura; Knight, Brogan E; Bogacz, Rafal; Whone, Alan; Coulthard, Elizabeth J

2017-01-01

Emerging evidence suggests that dopamine may modulate learning and memory with important implications for understanding the neurobiology of memory and future therapeutic targeting. An influential hypothesis posits that dopamine biases reinforcement learning. More recent data also suggest an influence during both consolidation and retrieval. Eighteen Parkinson’s disease patients learned through feedback ON or OFF medication, with memory tested 24 hr later ON or OFF medication (4 conditions, within-subjects design with matched healthy control group). Patients OFF medication during learning decreased in memory accuracy over the following 24 hr. In contrast to previous studies, however, dopaminergic medication during learning and testing did not affect expression of positive or negative reinforcement. Two further experiments were run without the 24 hr delay, but they too failed to reproduce effects of dopaminergic medication on reinforcement learning. While supportive of a dopaminergic role in consolidation, this study failed to replicate previous findings on reinforcement learning. DOI: http://dx.doi.org/10.7554/eLife.26801.001 PMID:28691905

Fear of losing money? Aversive conditioning with secondary reinforcers.

PubMed

Delgado, M R; Labouliere, C D; Phelps, E A

2006-12-01

Money is a secondary reinforcer that acquires its value through social communication and interaction. In everyday human behavior and laboratory studies, money has been shown to influence appetitive or reward learning. It is unclear, however, if money has a similar impact on aversive learning. The goal of this study was to investigate the efficacy of money in aversive learning, comparing it with primary reinforcers that are traditionally used in fear conditioning paradigms. A series of experiments were conducted in which participants initially played a gambling game that led to a monetary gain. They were then presented with an aversive conditioning paradigm, with either shock (primary reinforcer) or loss of money (secondary reinforcer) as the unconditioned stimulus. Skin conductance responses and subjective ratings indicated that potential monetary loss modulated the conditioned response. Depending on the presentation context, the secondary reinforcer was as effective as the primary reinforcer during aversive conditioning. These results suggest that stimuli that acquire reinforcing properties through social communication and interaction, such as money, can effectively influence aversive learning.

Reinforcement learning and Tourette syndrome.

PubMed

Palminteri, Stefano; Pessiglione, Mathias

2013-01-01

In this chapter, we report the first experimental explorations of reinforcement learning in Tourette syndrome, realized by our team in the last few years. This report will be preceded by an introduction aimed to provide the reader with the state of the art of the knowledge concerning the neural bases of reinforcement learning at the moment of these studies and the scientific rationale beyond them. In short, reinforcement learning is learning by trial and error to maximize rewards and minimize punishments. This decision-making and learning process implicates the dopaminergic system projecting to the frontal cortex-basal ganglia circuits. A large body of evidence suggests that the dysfunction of the same neural systems is implicated in the pathophysiology of Tourette syndrome. Our results show that Tourette condition, as well as the most common pharmacological treatments (dopamine antagonists), affects reinforcement learning performance in these patients. Specifically, the results suggest a deficit in negative reinforcement learning, possibly underpinned by a functional hyperdopaminergia, which could explain the persistence of tics, despite their evident inadaptive (negative) value. This idea, together with the implications of these results in Tourette therapy and the future perspectives, is discussed in Section 4 of this chapter. © 2013 Elsevier Inc. All rights reserved.

On the integration of reinforcement learning and approximate reasoning for control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

Intelligence moderates reinforcement learning: a mini-review of the neural evidence

PubMed Central

2014-01-01

Our understanding of the neural basis of reinforcement learning and intelligence, two key factors contributing to human strivings, has progressed significantly recently. However, the overlap of these two lines of research, namely, how intelligence affects neural responses during reinforcement learning, remains uninvestigated. A mini-review of three existing studies suggests that higher IQ (especially fluid IQ) may enhance the neural signal of positive prediction error in dorsolateral prefrontal cortex, dorsal anterior cingulate cortex, and striatum, several brain substrates of reinforcement learning or intelligence. PMID:25185818

Intelligence moderates reinforcement learning: a mini-review of the neural evidence.

PubMed

Chen, Chong

2015-06-01

Our understanding of the neural basis of reinforcement learning and intelligence, two key factors contributing to human strivings, has progressed significantly recently. However, the overlap of these two lines of research, namely, how intelligence affects neural responses during reinforcement learning, remains uninvestigated. A mini-review of three existing studies suggests that higher IQ (especially fluid IQ) may enhance the neural signal of positive prediction error in dorsolateral prefrontal cortex, dorsal anterior cingulate cortex, and striatum, several brain substrates of reinforcement learning or intelligence. Copyright © 2015 the American Physiological Society.

Prespeech motor learning in a neural network using reinforcement☆

PubMed Central

Warlaumont, Anne S.; Westermann, Gert; Buder, Eugene H.; Oller, D. Kimbrough

2012-01-01

Vocal motor development in infancy provides a crucial foundation for language development. Some significant early accomplishments include learning to control the process of phonation (the production of sound at the larynx) and learning to produce the sounds of one’s language. Previous work has shown that social reinforcement shapes the kinds of vocalizations infants produce. We present a neural network model that provides an account of how vocal learning may be guided by reinforcement. The model consists of a self-organizing map that outputs to muscles of a realistic vocalization synthesizer. Vocalizations are spontaneously produced by the network. If a vocalization meets certain acoustic criteria, it is reinforced, and the weights are updated to make similar muscle activations increasingly likely to recur. We ran simulations of the model under various reinforcement criteria and tested the types of vocalizations it produced after learning in the differ-ent conditions. When reinforcement was contingent on the production of phonated (i.e. voiced) sounds, the network’s post learning productions were almost always phonated, whereas when reinforcement was not contingent on phonation, the network’s post-learning productions were almost always not phonated. When reinforcement was contingent on both phonation and proximity to English vowels as opposed to Korean vowels, the model’s post-learning productions were more likely to resemble the English vowels and vice versa. PMID:23275137

Investigating implicit statistical learning mechanisms through contextual cueing.

PubMed

Goujon, Annabelle; Didierjean, André; Thorpe, Simon

2015-09-01

Since its inception, the contextual cueing (CC) paradigm has generated considerable interest in various fields of cognitive sciences because it constitutes an elegant approach to understanding how statistical learning (SL) mechanisms can detect contextual regularities during a visual search. In this article we review and discuss five aspects of CC: (i) the implicit nature of learning, (ii) the mechanisms involved in CC, (iii) the mediating factors affecting CC, (iv) the generalization of CC phenomena, and (v) the dissociation between implicit and explicit CC phenomena. The findings suggest that implicit SL is an inherent component of ongoing processing which operates through clustering, associative, and reinforcement processes at various levels of sensory-motor processing, and might result from simple spike-timing-dependent plasticity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reinforcement learning in complementarity game and population dynamics

NASA Astrophysics Data System (ADS)

Jost, Jürgen; Li, Wei

2014-02-01

We systematically test and compare different reinforcement learning schemes in a complementarity game [J. Jost and W. Li, Physica A 345, 245 (2005), 10.1016/j.physa.2004.07.005] played between members of two populations. More precisely, we study the Roth-Erev, Bush-Mosteller, and SoftMax reinforcement learning schemes. A modified version of Roth-Erev with a power exponent of 1.5, as opposed to 1 in the standard version, performs best. We also compare these reinforcement learning strategies with evolutionary schemes. This gives insight into aspects like the issue of quick adaptation as opposed to systematic exploration or the role of learning rates.

Reinforcement Learning and Dopamine in Schizophrenia: Dimensions of Symptoms or Specific Features of a Disease Group?

PubMed Central

Deserno, Lorenz; Boehme, Rebecca; Heinz, Andreas; Schlagenhauf, Florian

2013-01-01

Abnormalities in reinforcement learning are a key finding in schizophrenia and have been proposed to be linked to elevated levels of dopamine neurotransmission. Behavioral deficits in reinforcement learning and their neural correlates may contribute to the formation of clinical characteristics of schizophrenia. The ability to form predictions about future outcomes is fundamental for environmental interactions and depends on neuronal teaching signals, like reward prediction errors. While aberrant prediction errors, that encode non-salient events as surprising, have been proposed to contribute to the formation of positive symptoms, a failure to build neural representations of decision values may result in negative symptoms. Here, we review behavioral and neuroimaging research in schizophrenia and focus on studies that implemented reinforcement learning models. In addition, we discuss studies that combined reinforcement learning with measures of dopamine. Thereby, we suggest how reinforcement learning abnormalities in schizophrenia may contribute to the formation of psychotic symptoms and may interact with cognitive deficits. These ideas point toward an interplay of more rigid versus flexible control over reinforcement learning. Pronounced deficits in the flexible or model-based domain may allow for a detailed characterization of well-established cognitive deficits in schizophrenia patients based on computational models of learning. Finally, we propose a framework based on the potentially crucial contribution of dopamine to dysfunctional reinforcement learning on the level of neural networks. Future research may strongly benefit from computational modeling but also requires further methodological improvement for clinical group studies. These research tools may help to improve our understanding of disease-specific mechanisms and may help to identify clinically relevant subgroups of the heterogeneous entity schizophrenia. PMID:24391603

Generalization of value in reinforcement learning by humans.

PubMed

Wimmer, G Elliott; Daw, Nathaniel D; Shohamy, Daphna

2012-04-01

Research in decision-making has focused on the role of dopamine and its striatal targets in guiding choices via learned stimulus-reward or stimulus-response associations, behavior that is well described by reinforcement learning theories. However, basic reinforcement learning is relatively limited in scope and does not explain how learning about stimulus regularities or relations may guide decision-making. A candidate mechanism for this type of learning comes from the domain of memory, which has highlighted a role for the hippocampus in learning of stimulus-stimulus relations, typically dissociated from the role of the striatum in stimulus-response learning. Here, we used functional magnetic resonance imaging and computational model-based analyses to examine the joint contributions of these mechanisms to reinforcement learning. Humans performed a reinforcement learning task with added relational structure, modeled after tasks used to isolate hippocampal contributions to memory. On each trial participants chose one of four options, but the reward probabilities for pairs of options were correlated across trials. This (uninstructed) relationship between pairs of options potentially enabled an observer to learn about option values based on experience with the other options and to generalize across them. We observed blood oxygen level-dependent (BOLD) activity related to learning in the striatum and also in the hippocampus. By comparing a basic reinforcement learning model to one augmented to allow feedback to generalize between correlated options, we tested whether choice behavior and BOLD activity were influenced by the opportunity to generalize across correlated options. Although such generalization goes beyond standard computational accounts of reinforcement learning and striatal BOLD, both choices and striatal BOLD activity were better explained by the augmented model. Consistent with the hypothesized role for the hippocampus in this generalization, functional connectivity between the ventral striatum and hippocampus was modulated, across participants, by the ability of the augmented model to capture participants' choice. Our results thus point toward an interactive model in which striatal reinforcement learning systems may employ relational representations typically associated with the hippocampus. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Instrumental learning and relearning in individuals with psychopathy and in patients with lesions involving the amygdala or orbitofrontal cortex.

PubMed

Mitchell, D G V; Fine, C; Richell, R A; Newman, C; Lumsden, J; Blair, K S; Blair, R J R

2006-05-01

Previous work has shown that individuals with psychopathy are impaired on some forms of associative learning, particularly stimulus-reinforcement learning (Blair et al., 2004; Newman & Kosson, 1986). Animal work suggests that the acquisition of stimulus-reinforcement associations requires the amygdala (Baxter & Murray, 2002). Individuals with psychopathy also show impoverished reversal learning (Mitchell, Colledge, Leonard, & Blair, 2002). Reversal learning is supported by the ventrolateral and orbitofrontal cortex (Rolls, 2004). In this paper we present experiments investigating stimulus-reinforcement learning and relearning in patients with lesions of the orbitofrontal cortex or amygdala, and individuals with developmental psychopathy without known trauma. The results are interpreted with reference to current neurocognitive models of stimulus-reinforcement learning, relearning, and developmental psychopathy. Copyright (c) 2006 APA, all rights reserved.

Model-based reinforcement learning with dimension reduction.

PubMed

Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi

2016-12-01

The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control. Copyright © 2016 Elsevier Ltd. All rights reserved.

Reinforcement of Science Learning through Local Culture: A Delphi Study

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2008-01-01

This study aims to explore the ways to reinforce science learning through local culture by using Delphi technique. Twenty four participants in various fields of study were selected. The result of study provides a framework for reinforcement of science learning through local culture on the theme life and environment. (Contains 1 table.)

A parameter control method in reinforcement learning to rapidly follow unexpected environmental changes.

PubMed

Murakoshi, Kazushi; Mizuno, Junya

2004-11-01

In order to rapidly follow unexpected environmental changes, we propose a parameter control method in reinforcement learning that changes each of learning parameters in appropriate directions. We determine each appropriate direction on the basis of relationships between behaviors and neuromodulators by considering an emergency as a key word. Computer experiments show that the agents using our proposed method could rapidly respond to unexpected environmental changes, not depending on either two reinforcement learning algorithms (Q-learning and actor-critic (AC) architecture) or two learning problems (discontinuous and continuous state-action problems).

RM-SORN: a reward-modulated self-organizing recurrent neural network.

PubMed

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Partial Planning Reinforcement Learning

DTIC Science & Technology

2012-08-31

Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Reinforcement Learning, Bayesian Optimization, Active ... Learning , Action Model Learning, Decision Theoretic Assistance Prasad Tadepalli, Alan Fern Oregon State University Office of Sponsored Programs Oregon State

Reinforcement learning in scheduling

NASA Technical Reports Server (NTRS)

Dietterich, Tom G.; Ok, Dokyeong; Zhang, Wei; Tadepalli, Prasad

1994-01-01

The goal of this research is to apply reinforcement learning methods to real-world problems like scheduling. In this preliminary paper, we show that learning to solve scheduling problems such as the Space Shuttle Payload Processing and the Automatic Guided Vehicle (AGV) scheduling can be usefully studied in the reinforcement learning framework. We discuss some of the special challenges posed by the scheduling domain to these methods and propose some possible solutions we plan to implement.

Neural Basis of Reinforcement Learning and Decision Making

PubMed Central

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive.

PubMed

Otto, A Ross; Gershman, Samuel J; Markman, Arthur B; Daw, Nathaniel D

2013-05-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. In these accounts, a flexible but computationally expensive model-based reinforcement-learning system has been contrasted with a less flexible but more efficient model-free reinforcement-learning system. The factors governing which system controls behavior-and under what circumstances-are still unclear. Following the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrated that having human decision makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement-learning strategy. Further, we showed that, across trials, people negotiate the trade-off between the two systems dynamically as a function of concurrent executive-function demands, and people's choice latencies reflect the computational expenses of the strategy they employ. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources.

The Curse of Planning: Dissecting multiple reinforcement learning systems by taxing the central executive

PubMed Central

Otto, A. Ross; Gershman, Samuel J.; Markman, Arthur B.; Daw, Nathaniel D.

2013-01-01

A number of accounts of human and animal behavior posit the operation of parallel and competing valuation systems in the control of choice behavior. Along these lines, a flexible but computationally expensive model-based reinforcement learning system has been contrasted with a less flexible but more efficient model-free reinforcement learning system. The factors governing which system controls behavior—and under what circumstances—are still unclear. Based on the hypothesis that model-based reinforcement learning requires cognitive resources, we demonstrate that having human decision-makers perform a demanding secondary task engenders increased reliance on a model-free reinforcement learning strategy. Further, we show that across trials, people negotiate this tradeoff dynamically as a function of concurrent executive function demands and their choice latencies reflect the computational expenses of the strategy employed. These results demonstrate that competition between multiple learning systems can be controlled on a trial-by-trial basis by modulating the availability of cognitive resources. PMID:23558545

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

PubMed

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Is Teaching Simple Surgical Skills Using an Operant Learning Program More Effective Than Teaching by Demonstration?

PubMed

Levy, I Martin; Pryor, Karen W; McKeon, Theresa R

2016-04-01

A surgical procedure is a complex behavior that can be constructed from foundation or component behaviors. Both the component and the composite behaviors built from them are much more likely to recur if it they are reinforced (operant learning). Behaviors in humans have been successfully reinforced using the acoustic stimulus from a mechanical clicker, where the clicker serves as a conditioned reinforcer that communicates in a way that is language- and judgment-free; however, to our knowledge, the use of operant-learning principles has not been formally evaluated for acquisition of surgical skills. Two surgical tasks were taught and compared using two teaching strategies: (1) an operant learning methodology using a conditioned, acoustic reinforcer (a clicker) for positive reinforcement; and (2) a more classical approach using demonstration alone. Our goal was to determine whether a group that is taught a surgical skill using an operant learning procedure would more precisely perform that skill than a group that is taught by demonstration alone. Two specific behaviors, "tying the locking, sliding knot" and "making a low-angle drill hole," were taught to the 2014 Postgraduate Year (PGY)-1 class and first- and second-year medical students, using an operant learning procedure incorporating precise scripts along with acoustic feedback. The control groups, composed of PGY-1 and -2 nonorthopaedic surgical residents and first- and second-year medical students, were taught using demonstration alone. The precision and speed of each behavior was recorded for each individual by a single experienced surgeon, skilled in operant learning. The groups were then compared. The operant learning group achieved better precision tying the locking, sliding knot than did the control group. Twelve of the 12 test group learners tied the knot and precisely performed all six component steps, whereas only four of the 12 control group learners tied the knot and correctly performed all six component steps (the test group median was 10 [range, 10-10], the control group median was 0 [range, 0-10], p = 0.004). However, the median "time to tie the first knot" for the test group was longer than for the control group (test group median 271 seconds [range, 184-626 seconds], control group median 163 seconds [range 93-900 seconds], p = 0.017), whereas the "time to tie 10 of the locking, sliding knots" was the same for both groups (test group mean 95 seconds ± SD = 15 [range, 67-120 seconds], control group mean 95 seconds ± SD = 28 [range, 62-139 seconds], p = 0.996). For the low-angle drill hole test, the test group more consistently achieved the ideal six-step behavior for precisely drilling the low-angle hole compared with the control group (p = 0.006 for the median number of technique success comparison with an odds ratio [at the 95% confidence interval] of 82.3 [29.1-232.8]). The mean time to drill 10 low-angle holes was not different between the test group (mean 193 seconds ± SD = 26 [range, 153-222 seconds]) and the control group (mean 146 seconds ± SD = 63 [range, 114-294 seconds]) (p = 0.084). Operant learning occurs as the behavior is constructed and is highly reinforced with the result measured, not in the time saved, but in the ultimate outcome of an accurately built complex behavior. Level II, therapeutic study.

Modification of saccadic gain by reinforcement

PubMed Central

Paeye, Céline; Wallman, Josh

2011-01-01

Control of saccadic gain is often viewed as a simple compensatory process in which gain is adjusted over many trials by the postsaccadic retinal error, thereby maintaining saccadic accuracy. Here, we propose that gain might also be changed by a reinforcement process not requiring a visual error. To test this hypothesis, we used experimental paradigms in which retinal error was removed by extinguishing the target at the start of each saccade and either an auditory tone or the vision of the target on the fovea was provided as reinforcement after those saccades that met an amplitude criterion. These reinforcement procedures caused a progressive change in saccade amplitude in nearly all subjects, although the rate of adaptation differed greatly among subjects. When we reversed the contingencies and reinforced those saccades landing closer to the original target location, saccade gain changed back toward normal gain in most subjects. When subjects had saccades adapted first by reinforcement and a week later by conventional intrasaccadic step adaptation, both paradigms yielded similar degrees of gain changes and similar transfer to new amplitudes and to new starting positions of the target step as well as comparable rates of recovery. We interpret these changes in saccadic gain in the absence of postsaccadic retinal error as showing that saccade adaptation is not controlled by a single error signal. More generally, our findings suggest that normal saccade adaptation might involve general learning mechanisms rather than only specialized mechanisms for motor calibration. PMID:21525366

B-tree search reinforcement learning for model based intelligent agent

NASA Astrophysics Data System (ADS)

Bhuvaneswari, S.; Vignashwaran, R.

2013-03-01

Agents trained by learning techniques provide a powerful approximation of active solutions for naive approaches. In this study using B - Trees implying reinforced learning the data search for information retrieval is moderated to achieve accuracy with minimum search time. The impact of variables and tactics applied in training are determined using reinforcement learning. Agents based on these techniques perform satisfactory baseline and act as finite agents based on the predetermined model against competitors from the course.

Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.

1992-01-01

Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

Reinforcement learning in multidimensional environments relies on attention mechanisms.

PubMed

Niv, Yael; Daniel, Reka; Geana, Andra; Gershman, Samuel J; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C

2015-05-27

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this "representation learning" process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the "curse of dimensionality" in reinforcement learning. Copyright © 2015 the authors 0270-6474/15/358145-13$15.00/0.

Changes in corticostriatal connectivity during reinforcement learning in humans.

PubMed

Horga, Guillermo; Maia, Tiago V; Marsh, Rachel; Hao, Xuejun; Xu, Dongrong; Duan, Yunsuo; Tau, Gregory Z; Graniello, Barbara; Wang, Zhishun; Kangarlu, Alayar; Martinez, Diana; Packard, Mark G; Peterson, Bradley S

2015-02-01

Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants' choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning. © 2014 Wiley Periodicals, Inc.

A common neural circuit mechanism for internally guided and externally reinforced forms of motor learning.

PubMed

Hisey, Erin; Kearney, Matthew Gene; Mooney, Richard

2018-04-01

The complex skills underlying verbal and musical expression can be learned without external punishment or reward, indicating their learning is internally guided. The neural mechanisms that mediate internally guided learning are poorly understood, but a circuit comprising dopamine-releasing neurons in the midbrain ventral tegmental area (VTA) and their targets in the basal ganglia are important to externally reinforced learning. Juvenile zebra finches copy a tutor song in a process that is internally guided and, in adulthood, can learn to modify the fundamental frequency (pitch) of a target syllable in response to external reinforcement with white noise. Here we combined intersectional genetic ablation of VTA neurons, reversible blockade of dopamine receptors in the basal ganglia, and singing-triggered optogenetic stimulation of VTA terminals to establish that a common VTA-basal ganglia circuit enables internally guided song copying and externally reinforced syllable pitch learning.

Knockout crickets for the study of learning and memory: Dopamine receptor Dop1 mediates aversive but not appetitive reinforcement in crickets.

PubMed

Awata, Hiroko; Watanabe, Takahito; Hamanaka, Yoshitaka; Mito, Taro; Noji, Sumihare; Mizunami, Makoto

2015-11-02

Elucidation of reinforcement mechanisms in associative learning is an important subject in neuroscience. In mammals, dopamine neurons are thought to play critical roles in mediating both appetitive and aversive reinforcement. Our pharmacological studies suggested that octopamine and dopamine neurons mediate reward and punishment, respectively, in crickets, but recent studies in fruit-flies concluded that dopamine neurons mediates both reward and punishment, via the type 1 dopamine receptor Dop1. To resolve the discrepancy between studies in different insect species, we produced Dop1 knockout crickets using the CRISPR/Cas9 system and found that they are defective in aversive learning with sodium chloride punishment but not appetitive learning with water or sucrose reward. The results suggest that dopamine and octopamine neurons mediate aversive and appetitive reinforcement, respectively, in crickets. We suggest unexpected diversity in neurotransmitters mediating appetitive reinforcement between crickets and fruit-flies, although the neurotransmitter mediating aversive reinforcement is conserved. This study demonstrates usefulness of the CRISPR/Cas9 system for producing knockout animals for the study of learning and memory.

Social Cognition as Reinforcement Learning: Feedback Modulates Emotion Inference.

PubMed

Zaki, Jamil; Kallman, Seth; Wimmer, G Elliott; Ochsner, Kevin; Shohamy, Daphna

2016-09-01

Neuroscientific studies of social cognition typically employ paradigms in which perceivers draw single-shot inferences about the internal states of strangers. Real-world social inference features much different parameters: People often encounter and learn about particular social targets (e.g., friends) over time and receive feedback about whether their inferences are correct or incorrect. Here, we examined this process and, more broadly, the intersection between social cognition and reinforcement learning. Perceivers were scanned using fMRI while repeatedly encountering three social targets who produced conflicting visual and verbal emotional cues. Perceivers guessed how targets felt and received feedback about whether they had guessed correctly. Visual cues reliably predicted one target's emotion, verbal cues predicted a second target's emotion, and neither reliably predicted the third target's emotion. Perceivers successfully used this information to update their judgments over time. Furthermore, trial-by-trial learning signals-estimated using two reinforcement learning models-tracked activity in ventral striatum and ventromedial pFC, structures associated with reinforcement learning, and regions associated with updating social impressions, including TPJ. These data suggest that learning about others' emotions, like other forms of feedback learning, relies on domain-general reinforcement mechanisms as well as domain-specific social information processing.

Human-level control through deep reinforcement learning.

PubMed

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning

NASA Astrophysics Data System (ADS)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Probabilistic Reinforcement Learning in Adults with Autism Spectrum Disorders

PubMed Central

Solomon, Marjorie; Smith, Anne C.; Frank, Michael J.; Ly, Stanford; Carter, Cameron S.

2017-01-01

Background Autism spectrum disorders (ASDs) can be conceptualized as disorders of learning, however there have been few experimental studies taking this perspective. Methods We examined the probabilistic reinforcement learning performance of 28 adults with ASDs and 30 typically developing adults on a task requiring learning relationships between three stimulus pairs consisting of Japanese characters with feedback that was valid with different probabilities (80%, 70%, and 60%). Both univariate and Bayesian state–space data analytic methods were employed. Hypotheses were based on the extant literature as well as on neurobiological and computational models of reinforcement learning. Results Both groups learned the task after training. However, there were group differences in early learning in the first task block where individuals with ASDs acquired the most frequently accurately reinforced stimulus pair (80%) comparably to typically developing individuals; exhibited poorer acquisition of the less frequently reinforced 70% pair as assessed by state–space learning curves; and outperformed typically developing individuals on the near chance (60%) pair. Individuals with ASDs also demonstrated deficits in using positive feedback to exploit rewarded choices. Conclusions Results support the contention that individuals with ASDs are slower learners. Based on neurobiology and on the results of computational modeling, one interpretation of this pattern of findings is that impairments are related to deficits in flexible updating of reinforcement history as mediated by the orbito-frontal cortex, with spared functioning of the basal ganglia. This hypothesis about the pathophysiology of learning in ASDs can be tested using functional magnetic resonance imaging. PMID:21425243

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ketterer, S.P.

This manual is designed as a comprehensive hands-on instructional manual for learning the T{sub E}X* computer typesetting program in a classroom environment. Each section presents a new concept in careful detail, concluding with an exercise (T{sub E}Xercise) to reinforce the learning of the concept. The manual introduces the novice T{sub E}X user to the program's basic command structure, along with the concepts of grouping, producing accents, making font changes, and generating mathematical symbols. The T{sub E}Xercises guide the new user in generating text containing footnotes, multilevel lists, and hanging indentations, as well as in magnifying text for viewgraphs. Once themore » basic text generation is defined, the more advanced topics of formatting math equations and tables are explained. A full range of math capabilities is presented --- beginning with simple one-line equations, progressing through complex numbered and aligned equations, and concluding with matrices. The sections on table generation present the basic concepts in T{sub E}X's table-formatting program and then build on them. The new user first learns to construct simple tables, and with careful explanations and guidance, learns to add one new table enhancement at a time. By the conclusion of these sections, the user can construct tables with horizontal and vertical rules and with column entries that are paragraphs. 1 ref.« less

Extinction from a Rationalist Perspective

PubMed Central

Gallistel, C. R.

2012-01-01

The merging of the computational theory of mind and evolutionary thinking leads to a kind of rationalism, in which enduring truths about the world have become implicit in the computations that enable the brain to cope with the experienced world. The dead reckoning computation, for example, is implemented within the brains of animals as one of the mechanisms that enables them to learn where they are (Gallistel, 1990, 1995). It integrates a velocity signal with respect to a time signal. Thus, the manner in which position and velocity relate to one another in the world is reflected in the manner in which signals representing those variables are processed in the brain. I use principles of information theory and Bayesian inference to derive from other simple principles explanations for: 1) the failure of partial reinforcement to increase reinforcements to acquisition; 2) the partial reinforcement extinction effect; 3) spontaneous recovery; 4) renewal; 5) reinstatement; 6) resurgence (aka facilitated reacquisition). Like the principle underlying dead-reckoning, these principles are grounded in analytic considerations. They are the kind of enduring truths about the world that are likely to have shaped the brain's computations. PMID:22391153

Role of dopamine D2 receptors in human reinforcement learning.

PubMed

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-09-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

Role of Dopamine D2 Receptors in Human Reinforcement Learning

PubMed Central

Eisenegger, Christoph; Naef, Michael; Linssen, Anke; Clark, Luke; Gandamaneni, Praveen K; Müller, Ulrich; Robbins, Trevor W

2014-01-01

Influential neurocomputational models emphasize dopamine (DA) as an electrophysiological and neurochemical correlate of reinforcement learning. However, evidence of a specific causal role of DA receptors in learning has been less forthcoming, especially in humans. Here we combine, in a between-subjects design, administration of a high dose of the selective DA D2/3-receptor antagonist sulpiride with genetic analysis of the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male volunteers. In contrast to predictions of prevailing models emphasizing DA's pivotal role in learning via prediction errors, we found that sulpiride did not disrupt learning, but rather induced profound impairments in choice performance. The disruption was selective for stimuli indicating reward, whereas loss avoidance performance was unaffected. Effects were driven by volunteers with higher serum levels of the drug, and in those with genetically determined lower density of striatal DA D2 receptors. This is the clearest demonstration to date for a causal modulatory role of the DA D2 receptor in choice performance that might be distinct from learning. Our findings challenge current reward prediction error models of reinforcement learning, and suggest that classical animal models emphasizing a role of postsynaptic DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well. PMID:24713613

Reinforcement learning in computer vision

NASA Astrophysics Data System (ADS)

Bernstein, A. V.; Burnaev, E. V.

2018-04-01

Nowadays, machine learning has become one of the basic technologies used in solving various computer vision tasks such as feature detection, image segmentation, object recognition and tracking. In many applications, various complex systems such as robots are equipped with visual sensors from which they learn state of surrounding environment by solving corresponding computer vision tasks. Solutions of these tasks are used for making decisions about possible future actions. It is not surprising that when solving computer vision tasks we should take into account special aspects of their subsequent application in model-based predictive control. Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. In recent years, Reinforcement learning has been used both for solving such applied tasks as processing and analysis of visual information, and for solving specific computer vision problems such as filtering, extracting image features, localizing objects in scenes, and many others. The paper describes shortly the Reinforcement learning technology and its use for solving computer vision problems.

Reinforcement Learning in Multidimensional Environments Relies on Attention Mechanisms

PubMed Central

Daniel, Reka; Geana, Andra; Gershman, Samuel J.; Leong, Yuan Chang; Radulescu, Angela; Wilson, Robert C.

2015-01-01

In recent years, ideas from the computational field of reinforcement learning have revolutionized the study of learning in the brain, famously providing new, precise theories of how dopamine affects learning in the basal ganglia. However, reinforcement learning algorithms are notorious for not scaling well to multidimensional environments, as is required for real-world learning. We hypothesized that the brain naturally reduces the dimensionality of real-world problems to only those dimensions that are relevant to predicting reward, and conducted an experiment to assess by what algorithms and with what neural mechanisms this “representation learning” process is realized in humans. Our results suggest that a bilateral attentional control network comprising the intraparietal sulcus, precuneus, and dorsolateral prefrontal cortex is involved in selecting what dimensions are relevant to the task at hand, effectively updating the task representation through trial and error. In this way, cortical attention mechanisms interact with learning in the basal ganglia to solve the “curse of dimensionality” in reinforcement learning. PMID:26019331

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

From Recurrent Choice to Skill Learning: A Reinforcement-Learning Model

ERIC Educational Resources Information Center

Fu, Wai-Tat; Anderson, John R.

2006-01-01

The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of…

Stochastic Reinforcement Benefits Skill Acquisition

ERIC Educational Resources Information Center

Dayan, Eran; Averbeck, Bruno B.; Richmond, Barry J.; Cohen, Leonardo G.

2014-01-01

Learning complex skills is driven by reinforcement, which facilitates both online within-session gains and retention of the acquired skills. Yet, in ecologically relevant situations, skills are often acquired when mapping between actions and rewarding outcomes is unknown to the learning agent, resulting in reinforcement schedules of a stochastic…

A reward optimization method based on action subrewards in hierarchical reinforcement learning.

PubMed

Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

2014-01-01

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

Multi-agent Reinforcement Learning Model for Effective Action Selection

NASA Astrophysics Data System (ADS)

Youk, Sang Jo; Lee, Bong Keun

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocop Keep away which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

Pragmatically Framed Cross-Situational Noun Learning Using Computational Reinforcement Models

PubMed Central

Najnin, Shamima; Banerjee, Bonny

2018-01-01

Cross-situational learning and social pragmatic theories are prominent mechanisms for learning word meanings (i.e., word-object pairs). In this paper, the role of reinforcement is investigated for early word-learning by an artificial agent. When exposed to a group of speakers, the agent comes to understand an initial set of vocabulary items belonging to the language used by the group. Both cross-situational learning and social pragmatic theory are taken into account. As social cues, joint attention and prosodic cues in caregiver's speech are considered. During agent-caregiver interaction, the agent selects a word from the caregiver's utterance and learns the relations between that word and the objects in its visual environment. The “novel words to novel objects” language-specific constraint is assumed for computing rewards. The models are learned by maximizing the expected reward using reinforcement learning algorithms [i.e., table-based algorithms: Q-learning, SARSA, SARSA-λ, and neural network-based algorithms: Q-learning for neural network (Q-NN), neural-fitted Q-network (NFQ), and deep Q-network (DQN)]. Neural network-based reinforcement learning models are chosen over table-based models for better generalization and quicker convergence. Simulations are carried out using mother-infant interaction CHILDES dataset for learning word-object pairings. Reinforcement is modeled in two cross-situational learning cases: (1) with joint attention (Attentional models), and (2) with joint attention and prosodic cues (Attentional-prosodic models). Attentional-prosodic models manifest superior performance to Attentional ones for the task of word-learning. The Attentional-prosodic DQN outperforms existing word-learning models for the same task. PMID:29441027

Reinforcement learning improves behaviour from evaluative feedback

NASA Astrophysics Data System (ADS)

Littman, Michael L.

2015-05-01

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Reinforcement learning improves behaviour from evaluative feedback.

PubMed

Littman, Michael L

2015-05-28

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

The drift diffusion model as the choice rule in reinforcement learning.

PubMed

Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

2017-08-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

Can model-free reinforcement learning explain deontological moral judgments?

PubMed

Ayars, Alisabeth

2016-05-01

Dual-systems frameworks propose that moral judgments are derived from both an immediate emotional response, and controlled/rational cognition. Recently Cushman (2013) proposed a new dual-system theory based on model-free and model-based reinforcement learning. Model-free learning attaches values to actions based on their history of reward and punishment, and explains some deontological, non-utilitarian judgments. Model-based learning involves the construction of a causal model of the world and allows for far-sighted planning; this form of learning fits well with utilitarian considerations that seek to maximize certain kinds of outcomes. I present three concerns regarding the use of model-free reinforcement learning to explain deontological moral judgment. First, many actions that humans find aversive from model-free learning are not judged to be morally wrong. Moral judgment must require something in addition to model-free learning. Second, there is a dearth of evidence for central predictions of the reinforcement account-e.g., that people with different reinforcement histories will, all else equal, make different moral judgments. Finally, to account for the effect of intention within the framework requires certain assumptions which lack support. These challenges are reasonable foci for future empirical/theoretical work on the model-free/model-based framework. Copyright © 2016 Elsevier B.V. All rights reserved.

The drift diffusion model as the choice rule in reinforcement learning

PubMed Central

Frank, Michael J.

2017-01-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups. PMID:27966103

General functioning predicts reward and punishment learning in schizophrenia.

PubMed

Somlai, Zsuzsanna; Moustafa, Ahmed A; Kéri, Szabolcs; Myers, Catherine E; Gluck, Mark A

2011-04-01

Previous studies investigating feedback-driven reinforcement learning in patients with schizophrenia have provided mixed results. In this study, we explored the clinical predictors of reward and punishment learning using a probabilistic classification learning task. Patients with schizophrenia (n=40) performed similarly to healthy controls (n=30) on the classification learning task. However, more severe negative and general symptoms were associated with lower reward-learning performance, whereas poorer general psychosocial functioning was correlated with both lower reward- and punishment-learning performances. Multiple linear regression analyses indicated that general psychosocial functioning was the only significant predictor of reinforcement learning performance when education, antipsychotic dose, and positive, negative and general symptoms were included in the analysis. These results suggest a close relationship between reinforcement learning and general psychosocial functioning in schizophrenia. Published by Elsevier B.V.

Agent-based traffic management and reinforcement learning in congested intersection network.

DOT National Transportation Integrated Search

2012-08-01

This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task.

PubMed

Akam, Thomas; Costa, Rui; Dayan, Peter

2015-12-01

The recently developed 'two-step' behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects' investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues.

Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task

PubMed Central

Akam, Thomas; Costa, Rui; Dayan, Peter

2015-01-01

The recently developed ‘two-step’ behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects’ investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues. PMID:26657806

Operant conditioning of enhanced pain sensitivity by heat-pain titration.

PubMed

Becker, Susanne; Kleinböhl, Dieter; Klossika, Iris; Hölzl, Rupert

2008-11-15

Operant conditioning mechanisms have been demonstrated to be important in the development of chronic pain. Most experimental studies have investigated the operant modulation of verbal pain reports with extrinsic reinforcement, such as verbal reinforcement. Whether this reflects actual changes in the subjective experience of the nociceptive stimulus remained unclear. This study replicates and extends our previous demonstration that enhanced pain sensitivity to prolonged heat-pain stimulation could be learned in healthy participants through intrinsic reinforcement (contingent changes in nociceptive input) independent of verbal pain reports. In addition, we examine whether different magnitudes of reinforcement differentially enhance pain sensitivity using an operant heat-pain titration paradigm. It is based on the previously developed non-verbal behavioral discrimination task for the assessment of sensitization, which uses discriminative down- or up-regulation of stimulus temperatures in response to changes in subjective intensity. In operant heat-pain titration, this discriminative behavior and not verbal pain report was contingently reinforced or punished by acute decreases or increases in heat-pain intensity. The magnitude of reinforcement was varied between three groups: low (N1=13), medium (N2=11) and high reinforcement (N3=12). Continuous reinforcement was applied to acquire and train the operant behavior, followed by partial reinforcement to analyze the underlying learning mechanisms. Results demonstrated that sensitization to prolonged heat-pain stimulation was enhanced by operant learning within 1h. The extent of sensitization was directly dependent on the received magnitude of reinforcement. Thus, operant learning mechanisms based on intrinsic reinforcement may provide an explanation for the gradual development of sustained hypersensitivity during pain that is becoming chronic.

Resurgence of instrumental behavior after an abstinence contingency.

PubMed

Bouton, Mark E; Schepers, Scott T

2014-06-01

In resurgence, an extinguished instrumental behavior (R1) recovers when a behavior that has replaced it (R2) is also extinguished. The phenomenon may be relevant to understanding relapse that can occur after the termination of "contingency management" treatments, in which an unwanted behavior (e.g., substance abuse) is reduced by reinforcing an alternative behavior. When reinforcement is discontinued, the unwanted behavior might resurge. However, unlike most resurgence experiments, contingency management treatments also introduce a negative contingency, in which reinforcers are not delivered unless the client has abstained from the unwanted behavior. In two experiments with rats, we therefore examined the effects of adding a negative "abstinence" contingency to the resurgence design. During response elimination, R2 was not reinforced unless R1 had not been emitted for a minimum period of time (45, 90, or 135 s). In both experiments, adding such a contingency to simple R1 extinction reduced, but did not eliminate, resurgence. In Experiment 2, we found the same effect in a yoked group that could earn reinforcers for R2 at the same points in time as the negative-contingency group, but without the requirement to abstain from R1. Thus, the negative contingency per se did not contribute to the reduction in resurgence. These results suggest that the contingency reduced resurgence by making reinforcers more difficult to earn and more widely spaced in time. This could have allowed the animal to learn that R1 was extinguished in the "context" of infrequent reinforcement-a context more like that of resurgence testing. The results are thus consistent with a contextual (renewal) account of resurgence. The method might provide a better model of relapse after termination of a contingency management treatment.

Place preference and vocal learning rely on distinct reinforcers in songbirds.

PubMed

Murdoch, Don; Chen, Ruidong; Goldberg, Jesse H

2018-04-30

In reinforcement learning (RL) agents are typically tasked with maximizing a single objective function such as reward. But it remains poorly understood how agents might pursue distinct objectives at once. In machines, multiobjective RL can be achieved by dividing a single agent into multiple sub-agents, each of which is shaped by agent-specific reinforcement, but it remains unknown if animals adopt this strategy. Here we use songbirds to test if navigation and singing, two behaviors with distinct objectives, can be differentially reinforced. We demonstrate that strobe flashes aversively condition place preference but not song syllables. Brief noise bursts aversively condition song syllables but positively reinforce place preference. Thus distinct behavior-generating systems, or agencies, within a single animal can be shaped by correspondingly distinct reinforcement signals. Our findings suggest that spatially segregated vocal circuits can solve a credit assignment problem associated with multiobjective learning.

Dopamine-Dependent Reinforcement of Motor Skill Learning: Evidence from Gilles de la Tourette Syndrome

ERIC Educational Resources Information Center

Palminteri, Stefano; Lebreton, Mael; Worbe, Yulia; Hartmann, Andreas; Lehericy, Stephane; Vidailhet, Marie; Grabli, David; Pessiglione, Mathias

2011-01-01

Reinforcement learning theory has been extensively used to understand the neural underpinnings of instrumental behaviour. A central assumption surrounds dopamine signalling reward prediction errors, so as to update action values and ensure better choices in the future. However, educators may share the intuitive idea that reinforcements not only…

Machine Learning Control For Highly Reconfigurable High-Order Systems

DTIC Science & Technology

2015-01-02

develop and flight test a Reinforcement Learning based approach for autonomous tracking of ground targets using a fixed wing Unmanned...Reinforcement Learning - based algorithms are developed for learning agents’ time dependent dynamics while also learning to control them. Three algorithms...to a wide range of engineering- based problems . Implementation of these solutions, however, is often complicated by the hysteretic, non-linear,

Reinforcement and inference in cross-situational word learning.

PubMed

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Extinction of Chained Instrumental Behaviors: Effects of Procurement Extinction on Consumption Responding

PubMed Central

Thrailkill, Eric A.; Bouton, Mark E.

2015-01-01

Instrumental behavior often consists of sequences or chains of responses that minimally include procurement behaviors that enable subsequent consumption behaviors. In such chains, behavioral units are linked by access to one another and eventually to a primary reinforcer, such as food or a drug. The present experiments examined the effects of extinguishing procurement responding on consumption responding after training of a discriminated heterogeneous instrumental chain. Rats learned to make a procurement response (e.g., pressing a lever) in the presence of a distinctive discriminative stimulus; making that response led to the presentation of a second discriminative stimulus that set the occasion for a consumption response (e.g., pulling a chain), which then produced a food-pellet reinforcer. Experiment 1 showed that extinction of either the full procurement-consumption chain or procurement alone weakened the consumption response tested in isolation. Experiment 2 replicated the procurement extinction effect and further demonstrated that the opportunity to make the procurement response, as opposed to simple exposure to the procurement stimulus alone, was required. In Experiment 3, rats learned 2 distinct discriminated heterogeneous chains; extinction of 1 procurement response specifically weakened the consumption response that had been associated with it. The results suggest that learning to inhibit the procurement response may produce extinction of consumption responding through mediated extinction. The experiments suggest the importance of an associative analysis of instrumental behavior chains. PMID:25915751

Evolution with Reinforcement Learning in Negotiation

PubMed Central

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm. PMID:25048108

Evolution with reinforcement learning in negotiation.

PubMed

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm.

Overcoming Learned Helplessness in Community College Students.

ERIC Educational Resources Information Center

Roueche, John E.; Mink, Oscar G.

1982-01-01

Reviews research on the effects of repeated experiences of helplessness and on locus of control. Identifies conditions necessary for overcoming learned helplessness; i.e., the potential for learning to occur; consistent reinforcement; relevant, valued reinforcers; and favorable psychological situation. Recommends eight ways for teachers to…

Dopamine D2 Receptor Signaling in the Nucleus Accumbens Comprises a Metabolic-Cognitive Brain Interface Regulating Metabolic Components of Glucose Reinforcement.

PubMed

Michaelides, Michael; Miller, Michael L; DiNieri, Jennifer A; Gomez, Juan L; Schwartz, Elizabeth; Egervari, Gabor; Wang, Gene Jack; Mobbs, Charles V; Volkow, Nora D; Hurd, Yasmin L

2017-11-01

Appetitive drive is influenced by coordinated interactions between brain circuits that regulate reinforcement and homeostatic signals that control metabolism. Glucose modulates striatal dopamine (DA) and regulates appetitive drive and reinforcement learning. Striatal DA D2 receptors (D2Rs) also regulate reinforcement learning and are implicated in glucose-related metabolic disorders. Nevertheless, interactions between striatal D2R and peripheral glucose have not been previously described. Here we show that manipulations involving striatal D2R signaling coincide with perseverative and impulsive-like responding for sucrose, a disaccharide consisting of fructose and glucose. Fructose conveys orosensory (ie, taste) reinforcement but does not convey metabolic (ie, nutrient-derived) reinforcement. Glucose however conveys orosensory reinforcement but unlike fructose, it is a major metabolic energy source, underlies sustained reinforcement, and activates striatal circuitry. We found that mice with deletion of dopamine- and cAMP-regulated neuronal phosphoprotein (DARPP-32) exclusively in D2R-expressing cells exhibited preferential D2R changes in the nucleus accumbens (NAc), a striatal region that critically regulates sucrose reinforcement. These changes coincided with perseverative and impulsive-like responding for sucrose pellets and sustained reinforcement learning of glucose-paired flavors. These mice were also characterized by significant glucose intolerance (ie, impaired glucose utilization). Systemic glucose administration significantly attenuated sucrose operant responding and D2R activation or blockade in the NAc bidirectionally modulated blood glucose levels and glucose tolerance. Collectively, these results implicate NAc D2R in regulating both peripheral glucose levels and glucose-dependent reinforcement learning behaviors and highlight the notion that glucose metabolic impairments arising from disrupted NAc D2R signaling are involved in compulsive and perseverative feeding behaviors.

A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

DOE PAGES

Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

2015-01-31

Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

The Effects of Partial Reinforcement in the Acquisition and Extinction of Recurrent Serial Patterns.

ERIC Educational Resources Information Center

Dockstader, Steven L.

The purpose of these 2 experiments was to determine whether sequential response pattern behavior is affected by partial reinforcement in the same way as other behavior systems. The first experiment investigated the partial reinforcement extinction effects (PREE) in a sequential concept learning task where subjects were required to learn a…

Microstimulation of the Human Substantia Nigra Alters Reinforcement Learning

PubMed Central

Ramayya, Ashwin G.; Misra, Amrit

2014-01-01

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action–reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action–reward associations rather than stimulus–reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action–reward associations during reinforcement learning. PMID:24828643

An Investigation of Ways to Reduce the Failure Rate of Student Pilots during Flying Training in the Royal Australian Air Force.

DTIC Science & Technology

1987-09-01

Luthans (28) expanded the concept of learning as follows: 1. Learning involves a change, though not necessarily an improvement, in behaviour. Learning...that results in an unpleasant outcome is not likely to be repeated (36:244). Luthans and Kreitner (27) described the various forms of reinforcement as...four 33 alternatives (defined previously on page 24 and taken from Luthans ) of positive reinforcement, negative reinforcement, extinction and punishment

Mastery Learning through Individualized Instruction: A Reinforcement Strategy

ERIC Educational Resources Information Center

Sagy, John; Ravi, R.; Ananthasayanam, R.

2009-01-01

The present study attempts to gauge the effect of individualized instructional methods as a reinforcement strategy for mastery learning. Among various individualized instructional methods, the study focuses on PIM (Programmed Instructional Method) and CAIM (Computer Assisted Instruction Method). Mastery learning is a process where students achieve…

Working Memory and Reinforcement Schedule Jointly Determine Reinforcement Learning in Children: Potential Implications for Behavioral Parent Training

PubMed Central

Segers, Elien; Beckers, Tom; Geurts, Hilde; Claes, Laurence; Danckaerts, Marina; van der Oord, Saskia

2018-01-01

Introduction: Behavioral Parent Training (BPT) is often provided for childhood psychiatric disorders. These disorders have been shown to be associated with working memory impairments. BPT is based on operant learning principles, yet how operant principles shape behavior (through the partial reinforcement (PRF) extinction effect, i.e., greater resistance to extinction that is created when behavior is reinforced partially rather than continuously) and the potential role of working memory therein is scarcely studied in children. This study explored the PRF extinction effect and the role of working memory therein using experimental tasks in typically developing children. Methods: Ninety-seven children (age 6–10) completed a working memory task and an operant learning task, in which children acquired a response-sequence rule under either continuous or PRF (120 trials), followed by an extinction phase (80 trials). Data of 88 children were used for analysis. Results: The PRF extinction effect was confirmed: We observed slower acquisition and extinction in the PRF condition as compared to the continuous reinforcement (CRF) condition. Working memory was negatively related to acquisition but not extinction performance. Conclusion: Both reinforcement contingencies and working memory relate to acquisition performance. Potential implications for BPT are that decreasing working memory load may enhance the chance of optimally learning through reinforcement. PMID:29643822

Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

PubMed Central

Doll, Bradley B.; Jacobs, W. Jake; Sanfey, Alan G.; Frank, Michael J.

2011-01-01

Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is “overridden” at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract “Q-learning” and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a “confirmation bias” in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes. PMID:19595993

How partial reinforcement of food cues affects the extinction and reacquisition of appetitive responses. A new model for dieting success?

PubMed

van den Akker, Karolien; Havermans, Remco C; Bouton, Mark E; Jansen, Anita

2014-10-01

Animals and humans can easily learn to associate an initially neutral cue with food intake through classical conditioning, but extinction of learned appetitive responses can be more difficult. Intermittent or partial reinforcement of food cues causes especially persistent behaviour in animals: after exposure to such learning schedules, the decline in responding that occurs during extinction is slow. After extinction, increases in responding with renewed reinforcement of food cues (reacquisition) might be less rapid after acquisition with partial reinforcement. In humans, it may be that the eating behaviour of some individuals resembles partial reinforcement schedules to a greater extent, possibly affecting dieting success by interacting with extinction and reacquisition. Furthermore, impulsivity has been associated with less successful dieting, and this association might be explained by impulsivity affecting the learning and extinction of appetitive responses. In the present two studies, the effects of different reinforcement schedules and impulsivity on the acquisition, extinction, and reacquisition of appetitive responses were investigated in a conditioning paradigm involving food rewards in healthy humans. Overall, the results indicate both partial reinforcement schedules and, possibly, impulsivity to be associated with worse extinction performance. A new model of dieting success is proposed: learning histories and, perhaps, certain personality traits (impulsivity) can interfere with the extinction and reacquisition of appetitive responses to food cues and they may be causally related to unsuccessful dieting. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regulating recognition decisions through incremental reinforcement learning.

PubMed

Han, Sanghoon; Dobbins, Ian G

2009-06-01

Does incremental reinforcement learning influence recognition memory judgments? We examined this question by subtly altering the relative validity or availability of feedback in order to differentially reinforce old or new recognition judgments. Experiment 1 probabilistically and incorrectly indicated that either misses or false alarms were correct in the context of feedback that was otherwise accurate. Experiment 2 selectively withheld feedback for either misses or false alarms in the context of feedback that was otherwise present. Both manipulations caused prominent shifts of recognition memory decision criteria that remained for considerable periods even after feedback had been altogether removed. Overall, these data demonstrate that incremental reinforcement-learning mechanisms influence the degree of caution subjects exercise when evaluating explicit memories.

Infant Contingency Learning in Different Cultural Contexts

ERIC Educational Resources Information Center

Graf, Frauke; Lamm, Bettina; Goertz, Claudia; Kolling, Thorsten; Freitag, Claudia; Spangler, Sibylle; Fassbender, Ina; Teubert, Manuel; Vierhaus, Marc; Keller, Heidi; Lohaus, Arnold; Schwarzer, Gudrun; Knopf, Monika

2012-01-01

Three-month-old Cameroonian Nso farmer and German middle-class infants were compared regarding learning and retention in a computerized mobile task. Infants achieving a preset learning criterion during reinforcement were tested for immediate and long-term retention measured in terms of an increased response rate after reinforcement and after a…

Adaptive Educational Software by Applying Reinforcement Learning

ERIC Educational Resources Information Center

Bennane, Abdellah

2013-01-01

The introduction of the intelligence in teaching software is the object of this paper. In software elaboration process, one uses some learning techniques in order to adapt the teaching software to characteristics of student. Generally, one uses the artificial intelligence techniques like reinforcement learning, Bayesian network in order to adapt…

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Extinction from a rationalist perspective.

PubMed

Gallistel, C R

2012-05-01

The merging of the computational theory of mind and evolutionary thinking leads to a kind of rationalism, in which enduring truths about the world have become implicit in the computations that enable the brain to cope with the experienced world. The dead reckoning computation, for example, is implemented within the brains of animals as one of the mechanisms that enables them to learn where they are (Gallistel, 1990, 1995). It integrates a velocity signal with respect to a time signal. Thus, the manner in which position and velocity relate to one another in the world is reflected in the manner in which signals representing those variables are processed in the brain. I use principles of information theory and Bayesian inference to derive from other simple principles explanations for: (1) the failure of partial reinforcement to increase reinforcements to acquisition; (2) the partial reinforcement extinction effect; (3) spontaneous recovery; (4) renewal; (5) reinstatement; (6) resurgence (aka facilitated reacquisition). Like the principle underlying dead-reckoning, these principles are grounded in analytic considerations. They are the kind of enduring truths about the world that are likely to have shaped the brain's computations. Copyright © 2012 Elsevier B.V. All rights reserved.

An Evaluation of the Value of Choice-Making Opportunities in Single-Operant Arrangements: Simple Fixed- and Progressive-Ratio Schedules

ERIC Educational Resources Information Center

Tiger, Jeffrey H.; Toussaint, Karen A.; Roath, Christopher T.

2010-01-01

The current study compared the effects of choice and no-choice reinforcement conditions on the task responding of 3 children with autism across 2 single-operant paradigm reinforcer assessments. The first assessment employed simple fixed-ratio (FR) schedules; the second used progressive-ratio (PR) schedules. The latter assessment identified the…

[Learning and memory in Drosophila: physiologic and genetic bases].

PubMed

Zhuravlev, A V; Nikitina, E A; Savvateeva-Popova, E V

2015-01-01

Elucidation of molecular mechanisms of cognitive functions is one of the major achievements in neurobiology. At most, this is due to the studies on the simple nervous systems, such as the CNS in Drosophila melanogaster. Many of its functional characteristics are pretty similar to higher vertebrates. Among these are: 1) evolutionary conservation of genes and molecular systems involved in the regulation of learning acquisition and memory formation; 2) presence of highly specialized and differentiated sensory, associative and motor centers; 3) utilization of similar modes of informational coding and analysis; 4) availability of major learning forms including non-associative, as well as associative learning; 5) diversity of different memories, including short-term- and protein synthesis- dependent long-term memory; 6) presence of aminergic reinforcement systems in the brain; 7) feed-back loops of circadian clocks, current organism experience and individual organism characters affecting cognitive process per se. In this review the main attention is paid to the two mostly studied Drosophila learning forms, namely to olfactory Iearning and courtship suppression conditioning (CCS). A separate consideration is given to the impacts of kynurenins and metabolite of actin remodeling signal cascade.

A spiking neural network model of model-free reinforcement learning with high-dimensional sensory input and perceptual ambiguity.

PubMed

Nakano, Takashi; Otsuka, Makoto; Yoshimoto, Junichiro; Doya, Kenji

2015-01-01

A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach.

Punishment insensitivity and impaired reinforcement learning in preschoolers.

PubMed

Briggs-Gowan, Margaret J; Nichols, Sara R; Voss, Joel; Zobel, Elvira; Carter, Alice S; McCarthy, Kimberly J; Pine, Daniel S; Blair, James; Wakschlag, Lauren S

2014-01-01

Youth and adults with psychopathic traits display disrupted reinforcement learning. Advances in measurement now enable examination of this association in preschoolers. The current study examines relations between reinforcement learning in preschoolers and parent ratings of reduced responsiveness to socialization, conceptualized as a developmental vulnerability to psychopathic traits. One hundred and fifty-seven preschoolers (mean age 4.7 ± 0.8 years) participated in a substudy that was embedded within a larger project. Children completed the 'Stars-in-Jars' task, which involved learning to select rewarded jars and avoid punished jars. Maternal report of responsiveness to socialization was assessed with the Punishment Insensitivity and Low Concern for Others scales of the Multidimensional Assessment of Preschool Disruptive Behavior (MAP-DB). Punishment Insensitivity, but not Low Concern for Others, was significantly associated with reinforcement learning in multivariate models that accounted for age and sex. Specifically, higher Punishment Insensitivity was associated with significantly lower overall performance and more errors on punished trials ('passive avoidance'). Impairments in reinforcement learning manifest in preschoolers who are high in maternal ratings of Punishment Insensitivity. If replicated, these findings may help to pinpoint the neurodevelopmental antecedents of psychopathic tendencies and suggest novel intervention targets beginning in early childhood. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

A Spiking Neural Network Model of Model-Free Reinforcement Learning with High-Dimensional Sensory Input and Perceptual Ambiguity

PubMed Central

Nakano, Takashi; Otsuka, Makoto; Yoshimoto, Junichiro; Doya, Kenji

2015-01-01

A theoretical framework of reinforcement learning plays an important role in understanding action selection in animals. Spiking neural networks provide a theoretically grounded means to test computational hypotheses on neurally plausible algorithms of reinforcement learning through numerical simulation. However, most of these models cannot handle observations which are noisy, or occurred in the past, even though these are inevitable and constraining features of learning in real environments. This class of problem is formally known as partially observable reinforcement learning (PORL) problems. It provides a generalization of reinforcement learning to partially observable domains. In addition, observations in the real world tend to be rich and high-dimensional. In this work, we use a spiking neural network model to approximate the free energy of a restricted Boltzmann machine and apply it to the solution of PORL problems with high-dimensional observations. Our spiking network model solves maze tasks with perceptually ambiguous high-dimensional observations without knowledge of the true environment. An extended model with working memory also solves history-dependent tasks. The way spiking neural networks handle PORL problems may provide a glimpse into the underlying laws of neural information processing which can only be discovered through such a top-down approach. PMID:25734662

Antipsychotic dose modulates behavioral and neural responses to feedback during reinforcement learning in schizophrenia.

PubMed

Insel, Catherine; Reinen, Jenna; Weber, Jochen; Wager, Tor D; Jarskog, L Fredrik; Shohamy, Daphna; Smith, Edward E

2014-03-01

Schizophrenia is characterized by an abnormal dopamine system, and dopamine blockade is the primary mechanism of antipsychotic treatment. Consistent with the known role of dopamine in reward processing, prior research has demonstrated that patients with schizophrenia exhibit impairments in reward-based learning. However, it remains unknown how treatment with antipsychotic medication impacts the behavioral and neural signatures of reinforcement learning in schizophrenia. The goal of this study was to examine whether antipsychotic medication modulates behavioral and neural responses to prediction error coding during reinforcement learning. Patients with schizophrenia completed a reinforcement learning task while undergoing functional magnetic resonance imaging. The task consisted of two separate conditions in which participants accumulated monetary gain or avoided monetary loss. Behavioral results indicated that antipsychotic medication dose was associated with altered behavioral approaches to learning, such that patients taking higher doses of medication showed increased sensitivity to negative reinforcement. Higher doses of antipsychotic medication were also associated with higher learning rates (LRs), suggesting that medication enhanced sensitivity to trial-by-trial feedback. Neuroimaging data demonstrated that antipsychotic dose was related to differences in neural signatures of feedback prediction error during the loss condition. Specifically, patients taking higher doses of medication showed attenuated prediction error responses in the striatum and the medial prefrontal cortex. These findings indicate that antipsychotic medication treatment may influence motivational processes in patients with schizophrenia.

Microstimulation of the human substantia nigra alters reinforcement learning.

PubMed

Ramayya, Ashwin G; Misra, Amrit; Baltuch, Gordon H; Kahana, Michael J

2014-05-14

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action-reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action-reward associations rather than stimulus-reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action-reward associations during reinforcement learning. Copyright © 2014 the authors 0270-6474/14/346887-09$15.00/0.

Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories

PubMed Central

Fonteneau, Raphael; Murphy, Susan A.; Wehenkel, Louis; Ernst, Damien

2013-01-01

In this paper, we consider the batch mode reinforcement learning setting, where the central problem is to learn from a sample of trajectories a policy that satisfies or optimizes a performance criterion. We focus on the continuous state space case for which usual resolution schemes rely on function approximators either to represent the underlying control problem or to represent its value function. As an alternative to the use of function approximators, we rely on the synthesis of “artificial trajectories” from the given sample of trajectories, and show that this idea opens new avenues for designing and analyzing algorithms for batch mode reinforcement learning. PMID:24049244

Human reinforcement learning subdivides structured action spaces by learning effector-specific values

PubMed Central

Gershman, Samuel J.; Pesaran, Bijan; Daw, Nathaniel D.

2009-01-01

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable, due to the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning – such as prediction error signals for action valuation associated with dopamine and the striatum – can cope with this “curse of dimensionality.” We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and BOLD activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to “divide and conquer” reinforcement learning over high-dimensional action spaces. PMID:19864565

Human reinforcement learning subdivides structured action spaces by learning effector-specific values.

PubMed

Gershman, Samuel J; Pesaran, Bijan; Daw, Nathaniel D

2009-10-28

Humans and animals are endowed with a large number of effectors. Although this enables great behavioral flexibility, it presents an equally formidable reinforcement learning problem of discovering which actions are most valuable because of the high dimensionality of the action space. An unresolved question is how neural systems for reinforcement learning-such as prediction error signals for action valuation associated with dopamine and the striatum-can cope with this "curse of dimensionality." We propose a reinforcement learning framework that allows for learned action valuations to be decomposed into effector-specific components when appropriate to a task, and test it by studying to what extent human behavior and blood oxygen level-dependent (BOLD) activity can exploit such a decomposition in a multieffector choice task. Subjects made simultaneous decisions with their left and right hands and received separate reward feedback for each hand movement. We found that choice behavior was better described by a learning model that decomposed the values of bimanual movements into separate values for each effector, rather than a traditional model that treated the bimanual actions as unitary with a single value. A decomposition of value into effector-specific components was also observed in value-related BOLD signaling, in the form of lateralized biases in striatal correlates of prediction error and anticipatory value correlates in the intraparietal sulcus. These results suggest that the human brain can use decomposed value representations to "divide and conquer" reinforcement learning over high-dimensional action spaces.

Separation of time-based and trial-based accounts of the partial reinforcement extinction effect.

PubMed

Bouton, Mark E; Woods, Amanda M; Todd, Travis P

2014-01-01

Two appetitive conditioning experiments with rats examined time-based and trial-based accounts of the partial reinforcement extinction effect (PREE). In the PREE, the loss of responding that occurs in extinction is slower when the conditioned stimulus (CS) has been paired with a reinforcer on some of its presentations (partially reinforced) instead of every presentation (continuously reinforced). According to a time-based or "time-accumulation" view (e.g., Gallistel and Gibbon, 2000), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger amount of time has accumulated in the CS over trials. In contrast, according to a trial-based view (e.g., Capaldi, 1967), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger number of CS presentations. Experiment 1 used a procedure that equated partially and continuously reinforced groups on their expected times to reinforcement during conditioning. A PREE was still observed. Experiment 2 then used an extinction procedure that allowed time in the CS and the number of trials to accumulate differentially through extinction. The PREE was still evident when responding was examined as a function of expected time units to the reinforcer, but was eliminated when responding was examined as a function of expected trial units to the reinforcer. There was no evidence that the animal responded according to the ratio of time accumulated during the CS in extinction over the time in the CS expected before the reinforcer. The results thus favor a trial-based account over a time-based account of extinction and the PREE. This article is part of a Special Issue entitled: Associative and Temporal Learning. Copyright © 2013 Elsevier B.V. All rights reserved.

Autonomous reinforcement learning with experience replay.

PubMed

Wawrzyński, Paweł; Tanwani, Ajay Kumar

2013-05-01

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time. Copyright © 2012 Elsevier Ltd. All rights reserved.

Electrophysiological correlates of reinforcement learning in young people with Tourette syndrome with and without co-occurring ADHD symptoms.

PubMed

Shephard, Elizabeth; Jackson, Georgina M; Groom, Madeleine J

2016-06-01

Altered reinforcement learning is implicated in the causes of Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD). TS and ADHD frequently co-occur but how this affects reinforcement learning has not been investigated. We examined the ability of young people with TS (n=18), TS+ADHD (N=17), ADHD (n=13) and typically developing controls (n=20) to learn and reverse stimulus-response (S-R) associations based on positive and negative reinforcement feedback. We used a 2 (TS-yes, TS-no)×2 (ADHD-yes, ADHD-no) factorial design to assess the effects of TS, ADHD, and their interaction on behavioural (accuracy, RT) and event-related potential (stimulus-locked P3, feedback-locked P2, feedback-related negativity, FRN) indices of learning and reversing the S-R associations. TS was associated with intact learning and reversal performance and largely typical ERP amplitudes. ADHD was associated with lower accuracy during S-R learning and impaired reversal learning (significantly reduced accuracy and a trend for smaller P3 amplitude). The results indicate that co-occurring ADHD symptoms impair reversal learning in TS+ADHD. The implications of these findings for behavioural tic therapies are discussed. Copyright © 2016 ISDN. Published by Elsevier Ltd. All rights reserved.

Utilising reinforcement learning to develop strategies for driving auditory neural implants.

PubMed

Lee, Geoffrey W; Zambetta, Fabio; Li, Xiaodong; Paolini, Antonio G

2016-08-01

In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies. By recording a comprehensive set of acoustic frequency presentations and neural responses from a set of animals we created a large database of neural responses to acoustic stimulation. Extensive electrical stimulation in the CN and the recording of neural responses in the IC provides a mapping of how the auditory system responds to electrical stimuli. The combined dataset is used as the foundation for the simulator, which is used to implement and test learning algorithms. Reinforcement learning, utilising a modified n-Armed Bandit solution, is implemented to demonstrate the model's function. We show the ability to effectively learn stimulation patterns which mimic the cochlea's ability to covert acoustic frequencies to neural activity. Time taken to learn effective replication using neural stimulation takes less than 20 min under continuous testing. These results show the utility of reinforcement learning in the field of neural stimulation. These results can be coupled with existing sound processing technologies to develop new auditory prosthetics that are adaptable to the recipients current auditory pathway. The same process can theoretically be abstracted to other sensory and motor systems to develop similar electrical replication of neural signals.

Embedded Incremental Feature Selection for Reinforcement Learning

DTIC Science & Technology

2012-05-01

Prior to this work, feature selection for reinforce- ment learning has focused on linear value function ap- proximation ( Kolter and Ng, 2009; Parr et al...InProceed- ings of the the 23rd International Conference on Ma- chine Learning, pages 449–456. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature

Social Learning, Reinforcement and Crime: Evidence from Three European Cities

ERIC Educational Resources Information Center

Tittle, Charles R.; Antonaccio, Olena; Botchkovar, Ekaterina

2012-01-01

This study reports a cross-cultural test of Social Learning Theory using direct measures of social learning constructs and focusing on the causal structure implied by the theory. Overall, the results strongly confirm the main thrust of the theory. Prior criminal reinforcement and current crime-favorable definitions are highly related in all three…

Novelty and Inductive Generalization in Human Reinforcement Learning

PubMed Central

Gershman, Samuel J.; Niv, Yael

2015-01-01

In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: what is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: how can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of reinforcement learning in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional reinforcement learning algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. PMID:25808176

Learning with incomplete information and the mathematical structure behind it.

PubMed

Kühn, Reimer; Stamatescu, Ion-Olimpiu

2007-07-01

We investigate the problem of learning with incomplete information as exemplified by learning with delayed reinforcement. We study a two phase learning scenario in which a phase of Hebbian associative learning based on momentary internal representations is supplemented by an 'unlearning' phase depending on a graded reinforcement signal. The reinforcement signal quantifies the success-rate globally for a number of learning steps in phase one, and 'unlearning' is indiscriminate with respect to associations learnt in that phase. Learning according to this model is studied via simulations and analytically within a student-teacher scenario for both single layer networks and, for a committee machine. Success and speed of learning depend on the ratio lambda of the learning rates used for the associative Hebbian learning phase and for the unlearning-correction in response to the reinforcement signal, respectively. Asymptotically perfect generalization is possible only, if this ratio exceeds a critical value lambda( c ), in which case the generalization error exhibits a power law decay with the number of examples seen by the student, with an exponent that depends in a non-universal manner on the parameter lambda. We find these features to be robust against a wide spectrum of modifications of microscopic modelling details. Two illustrative applications-one of a robot learning to navigate a field containing obstacles, and the problem of identifying a specific component in a collection of stimuli-are also provided.

Resurgence of instrumental behavior after an abstinence contingency

PubMed Central

Bouton, Mark E.; Schepers, Scott T.

2014-01-01

In resurgence, an extinguished instrumental behavior (R1) recovers when a behavior that replaced it (R2) is also extinguished. The phenomenon may be relevant to understanding relapse that can occur after the termination of “contingency management” treatments, in which unwanted behavior (e.g., substance abuse) is reduced by reinforcing alternative behavior. When reinforcement is discontinued, the unwanted behavior might resurge. However, unlike most resurgence experiments, contingency management treatments also introduce a negative contingency in which reinforcers are not delivered unless the client has abstained from the unwanted behavior. Two experiments with rats therefore examined the effects of adding a negative “abstinence” contingency to the resurgence design. During response elimination, R2 was not reinforced unless R1 had not been emitted for a minimum period of time (45, 90, or 135 s). In both experiments, adding such a contingency to simple R1 extinction reduced, but did not eliminate, resurgence. Experiment 2 found the same effect in a yoked group that could earn reinforcers for R2 at the same points in time, but without the requirement to abstain from R1. Thus, the negative contingency per se did not contribute. Results suggest that the contingency reduced resurgence by making reinforcers more difficult to earn and more widely spaced in time. This could have allowed the animal to learn that R1 was extinguished in the “context” of infrequent reinforcement—a context more like that of resurgence testing. The results are thus consistent with a contextual (renewal) account of resurgence. The method might provide a better model of relapse after termination of a contingency management treatment. PMID:24366673

Framing Reinforcement Learning from Human Reward: Reward Positivity, Temporal Discounting, Episodicity, and Performance

DTIC Science & Technology

2014-09-29

Framing Reinforcement Learning from Human Reward: Reward Positivity, Temporal Discounting, Episodicity , and Performance W. Bradley Knox...positive a trainer’s reward values are; temporal discounting, the extent to which future reward is discounted in value; episodicity , whether task...learning occurs in discrete learning episodes instead of one continuing session; and task performance, the agent’s performance on the task the trainer

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

Framework for robot skill learning using reinforcement learning

NASA Astrophysics Data System (ADS)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

Neural mechanisms of cue-approach training

PubMed Central

Bakkour, Akram; Lewis-Peacock, Jarrod A.; Poldrack, Russell A.; Schonberg, Tom

2016-01-01

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training. PMID:27677231

Proactivity and Reinforcement: The Contingency of Social Behavior

ERIC Educational Resources Information Center

Williams, J. Sherwood; And Others

1976-01-01

This paper analyzes development of group structure in terms of the stimulus-sampling perspective. Learning is the continual sampling of possibilities, with those reinforced possibilities increasing in probability of occurance. This contingency learning approach is tested experimentally. (NG)

Mobile robots exploration through cnn-based reinforcement learning.

PubMed

Tai, Lei; Liu, Ming

2016-01-01

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-networks model. Based on the recent success of deep Q-network on artificial intelligence, the robot controller achieved the exploration and obstacle avoidance abilities in several different simulated environments. It is the first time that the reinforcement learning is used to build an exploration strategy for mobile robots through raw sensor information.

Altered neural encoding of prediction errors in assault-related posttraumatic stress disorder.

PubMed

Ross, Marisa C; Lenow, Jennifer K; Kilts, Clinton D; Cisler, Josh M

2018-05-12

Posttraumatic stress disorder (PTSD) is widely associated with deficits in extinguishing learned fear responses, which relies on mechanisms of reinforcement learning (e.g., updating expectations based on prediction errors). However, the degree to which PTSD is associated with impairments in general reinforcement learning (i.e., outside of the context of fear stimuli) remains poorly understood. Here, we investigate brain and behavioral differences in general reinforcement learning between adult women with and without a current diagnosis of PTSD. 29 adult females (15 PTSD with exposure to assaultive violence, 14 controls) underwent a neutral reinforcement-learning task (i.e., two arm bandit task) during fMRI. We modeled participant behavior using different adaptations of the Rescorla-Wagner (RW) model and used Independent Component Analysis to identify timecourses for large-scale a priori brain networks. We found that an anticorrelated and risk sensitive RW model best fit participant behavior, with no differences in computational parameters between groups. Women in the PTSD group demonstrated significantly less neural encoding of prediction errors in both a ventral striatum/mPFC and anterior insula network compared to healthy controls. Weakened encoding of prediction errors in the ventral striatum/mPFC and anterior insula during a general reinforcement learning task, outside of the context of fear stimuli, suggests the possibility of a broader conceptualization of learning differences in PTSD than currently proposed in current neurocircuitry models of PTSD. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated Inattention and Fatigue Detection System in Distance Education for Elementary School Students

ERIC Educational Resources Information Center

Hwang, Kuo-An; Yang, Chia-Hao

2009-01-01

Most courses based on distance learning focus on the cognitive domain of learning. Because students are sometimes inattentive or tired, they may neglect the attention goal of learning. This study proposes an auto-detection and reinforcement mechanism for the distance-education system based on the reinforcement teaching strategy. If a student is…

When, What, and How Much to Reward in Reinforcement Learning-Based Models of Cognition

ERIC Educational Resources Information Center

Janssen, Christian P.; Gray, Wayne D.

2012-01-01

Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other…

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning

PubMed Central

McGregor, Heather R.; Mohatarem, Ayman

2017-01-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback. PMID:28753634

Dissociating error-based and reinforcement-based loss functions during sensorimotor learning.

PubMed

Cashaback, Joshua G A; McGregor, Heather R; Mohatarem, Ayman; Gribble, Paul L

2017-07-01

It has been proposed that the sensorimotor system uses a loss (cost) function to evaluate potential movements in the presence of random noise. Here we test this idea in the context of both error-based and reinforcement-based learning. In a reaching task, we laterally shifted a cursor relative to true hand position using a skewed probability distribution. This skewed probability distribution had its mean and mode separated, allowing us to dissociate the optimal predictions of an error-based loss function (corresponding to the mean of the lateral shifts) and a reinforcement-based loss function (corresponding to the mode). We then examined how the sensorimotor system uses error feedback and reinforcement feedback, in isolation and combination, when deciding where to aim the hand during a reach. We found that participants compensated differently to the same skewed lateral shift distribution depending on the form of feedback they received. When provided with error feedback, participants compensated based on the mean of the skewed noise. When provided with reinforcement feedback, participants compensated based on the mode. Participants receiving both error and reinforcement feedback continued to compensate based on the mean while repeatedly missing the target, despite receiving auditory, visual and monetary reinforcement feedback that rewarded hitting the target. Our work shows that reinforcement-based and error-based learning are separable and can occur independently. Further, when error and reinforcement feedback are in conflict, the sensorimotor system heavily weights error feedback over reinforcement feedback.

A Discussion of Possibility of Reinforcement Learning Using Event-Related Potential in BCI

NASA Astrophysics Data System (ADS)

Yamagishi, Yuya; Tsubone, Tadashi; Wada, Yasuhiro

Recently, Brain computer interface (BCI) which is a direct connecting pathway an external device such as a computer or a robot and a human brain have gotten a lot of attention. Since BCI can control the machines as robots by using the brain activity without using the voluntary muscle, the BCI may become a useful communication tool for handicapped persons, for instance, amyotrophic lateral sclerosis patients. However, in order to realize the BCI system which can perform precise tasks on various environments, it is necessary to design the control rules to adapt to the dynamic environments. Reinforcement learning is one approach of the design of the control rule. If this reinforcement leaning can be performed by the brain activity, it leads to the attainment of BCI that has general versatility. In this research, we paid attention to P300 of event-related potential as an alternative signal of the reward of reinforcement learning. We discriminated between the success and the failure trials from P300 of the EEG of the single trial by using the proposed discrimination algorithm based on Support vector machine. The possibility of reinforcement learning was examined from the viewpoint of the number of discriminated trials. It was shown that there was a possibility to be able to learn in most subjects.

Comparative learning theory and its application in the training of horses.

PubMed

Cooper, J J

1998-11-01

Training can best be explained as a process that occurs through stimulus-response-reinforcement chains, whereby animals are conditioned to associate cues in their environment, with specific behavioural responses and their rewarding consequences. Research into learning in horses has concentrated on their powers of discrimination and on primary positive reinforcement schedules, where the correct response is paired with a desirable consequence such as food. In contrast, a number of other learning processes that are used in training have been widely studied in other species, but have received little scientific investigation in the horse. These include: negative reinforcement, where performance of the correct response is followed by removal of, or decrease in, intensity of a unpleasant stimulus; punishment, where an incorrect response is paired with an undesirable consequence, but without consistent prior warning; secondary conditioning, where a natural primary reinforcer such as food is closely associated with an arbitrary secondary reinforcer such as vocal praise; and variable or partial conditioning, where once the correct response has been learnt, reinforcement is presented according to an intermittent schedule to increase resistance to extinction outside of training.

The nature of sexual reinforcement.

PubMed Central

Crawford, L L; Holloway, K S; Domjan, M

1993-01-01

Sexual reinforcers are not part of a regulatory system involved in the maintenance of critical metabolic processes, they differ for males and females, they differ as a function of species and mating system, and they show ontogenetic and seasonal changes related to endocrine conditions. Exposure to a member of the opposite sex without copulation can be sufficient for sexual reinforcement. However, copulatory access is a stronger reinforcer, and copulatory opportunity can serve to enhance the reinforcing efficacy of stimulus features of a sexual partner. Conversely, under certain conditions, noncopulatory exposure serves to decrease reinforcer efficacy. Many common learning phenomena such as acquisition, extinction, discrimination learning, second-order conditioning, and latent inhibition have been demonstrated in sexual conditioning. These observations extend the generality of findings obtained with more conventional reinforcers, but the mechanisms of these effects and their gender and species specificity remain to be explored. PMID:8354970

Collapse limit states of reinforced earth retaining walls

NASA Astrophysics Data System (ADS)

Bolton, M. D.; Pang, P. L. R.

The use of systems of earth reinforcement or anchorage is gaining in popularity. It therefore becomes important to assess whether the methods of design which were adopted for such constructions represent valid predictions of realistic limit states. Confidence can only be gained with regard to the effectiveness of limit state criteria if a wide variety of representative limit states were observed. Over 80 centrifugal model tests of simple reinforced earth retaining walls were carried out, with the main purpose of clarifying the nature of appropriate collapse criteria. Collapses due to an insufficiency of friction were shown to be repeatable and therefore subject to fairly simple limit state calculations.

Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

PubMed Central

Guggenmos, Matthias; Wilbertz, Gregor; Hebart, Martin N; Sterzer, Philipp

2016-01-01

It is well established that learning can occur without external feedback, yet normative reinforcement learning theories have difficulties explaining such instances of learning. Here, we propose that human observers are capable of generating their own feedback signals by monitoring internal decision variables. We investigated this hypothesis in a visual perceptual learning task using fMRI and confidence reports as a measure for this monitoring process. Employing a novel computational model in which learning is guided by confidence-based reinforcement signals, we found that mesolimbic brain areas encoded both anticipation and prediction error of confidence—in remarkable similarity to previous findings for external reward-based feedback. We demonstrate that the model accounts for choice and confidence reports and show that the mesolimbic confidence prediction error modulation derived through the model predicts individual learning success. These results provide a mechanistic neurobiological explanation for learning without external feedback by augmenting reinforcement models with confidence-based feedback. DOI: http://dx.doi.org/10.7554/eLife.13388.001 PMID:27021283

Active Learning session based on Didactical Engineering framework for conceptual change in students' equilibrium and stability understanding

NASA Astrophysics Data System (ADS)

Canu, Michael; Duque, Mauricio; de Hosson, Cécile

2017-01-01

Engineering students on control courses lack a deep understanding of equilibrium and stability that are crucial concepts in this discipline. Several studies have shown that students find it difficult to understand simple familiar or academic static equilibrium cases as well as dynamic ones from mechanics even if they know the discipline's criteria and formulae. Our aim is to study the impact of a specific and innovative classroom session, containing well-chosen situations that address students' misconceptions. We propose an example of Active Learning experiment based both on the Didactical Engineering methodology and the Conceptual Fields Theory that aims at promoting a conceptual change in students. The chosen methodology allows, at the same time, a proper design of the student learning activities, an accurate monitoring of the students' rational use during the tasks and provides an internal tool for the evaluation of the session's efficiency. Although the expected starting conceptual change was detected, it would require another activity in order to be reinforced.

Phasic dopamine as a prediction error of intrinsic and extrinsic reinforcements driving both action acquisition and reward maximization: a simulated robotic study.

PubMed

Mirolli, Marco; Santucci, Vieri G; Baldassarre, Gianluca

2013-03-01

An important issue of recent neuroscientific research is to understand the functional role of the phasic release of dopamine in the striatum, and in particular its relation to reinforcement learning. The literature is split between two alternative hypotheses: one considers phasic dopamine as a reward prediction error similar to the computational TD-error, whose function is to guide an animal to maximize future rewards; the other holds that phasic dopamine is a sensory prediction error signal that lets the animal discover and acquire novel actions. In this paper we propose an original hypothesis that integrates these two contrasting positions: according to our view phasic dopamine represents a TD-like reinforcement prediction error learning signal determined by both unexpected changes in the environment (temporary, intrinsic reinforcements) and biological rewards (permanent, extrinsic reinforcements). Accordingly, dopamine plays the functional role of driving both the discovery and acquisition of novel actions and the maximization of future rewards. To validate our hypothesis we perform a series of experiments with a simulated robotic system that has to learn different skills in order to get rewards. We compare different versions of the system in which we vary the composition of the learning signal. The results show that only the system reinforced by both extrinsic and intrinsic reinforcements is able to reach high performance in sufficiently complex conditions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory.

PubMed

Collins, Anne G E; Frank, Michael J

2018-03-06

Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.

Learning and tuning fuzzy logic controllers through reinforcements.

PubMed

Berenji, H R; Khedkar, P

1992-01-01

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Impairments in action-outcome learning in schizophrenia.

PubMed

Morris, Richard W; Cyrzon, Chad; Green, Melissa J; Le Pelley, Mike E; Balleine, Bernard W

2018-03-03

Learning the causal relation between actions and their outcomes (AO learning) is critical for goal-directed behavior when actions are guided by desire for the outcome. This can be contrasted with habits that are acquired by reinforcement and primed by prevailing stimuli, in which causal learning plays no part. Recently, we demonstrated that goal-directed actions are impaired in schizophrenia; however, whether this deficit exists alongside impairments in habit or reinforcement learning is unknown. The present study distinguished deficits in causal learning from reinforcement learning in schizophrenia. We tested people with schizophrenia (SZ, n = 25) and healthy adults (HA, n = 25) in a vending machine task. Participants learned two action-outcome contingencies (e.g., push left to get a chocolate M&M, push right to get a cracker), and they also learned one contingency was degraded by delivery of noncontingent outcomes (e.g., free M&Ms), as well as changes in value by outcome devaluation. Both groups learned the best action to obtain rewards; however, SZ did not distinguish the more causal action when one AO contingency was degraded. Moreover, action selection in SZ was insensitive to changes in outcome value unless feedback was provided, and this was related to the deficit in AO learning. The failure to encode the causal relation between action and outcome in schizophrenia occurred without any apparent deficit in reinforcement learning. This implies that poor goal-directed behavior in schizophrenia cannot be explained by a more primary deficit in reward learning such as insensitivity to reward value or reward prediction errors.

The Effects of a Token Reinforcement System on the Reading and Arithmetic Skills Learnings of Migrant Primary School Pupils.

ERIC Educational Resources Information Center

Heitzman, Andrew J.

The New York State Center for Migrant Studies conducted this 1968 study which investigated effects of token reinforcers on reading and arithmetic skills learnings of migrant primary school students during a 6-week summer school session. Students (Negro and Caucasian) received plastic tokens to reward skills learning responses. Tokens were traded…

The Effects of Observation of Learn Units during Reinforcement and Correction Conditions on the Rate of Learning Math Algorithms by Fifth Grade Students

ERIC Educational Resources Information Center

Neu, Jessica Adele

2013-01-01

I conducted two studies on the comparative effects of the observation of learn units during (a) reinforcement or (b) correction conditions on the acquisition of math objectives. The dependent variables were the within-session cumulative numbers of correct responses emitted during observational sessions. The independent variables were the…

An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning Approach

ERIC Educational Resources Information Center

Chi, Min; VanLehn, Kurt; Litman, Diane; Jordan, Pamela

2011-01-01

Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to…

The Identification and Establishment of Reinforcement for Collaboration in Elementary Students

ERIC Educational Resources Information Center

Darcy, Laura

2017-01-01

In Experiment 1, I conducted a functional analysis of student rate of learning with and without a peer-yoked contingency for 12 students in Kindergarten through 2nd grade in order to determine if they had conditioned reinforcement for collaboration. Using an ABAB reversal design, I compared rate of learning as measured by learn units to criterion…

Stress enhances model-free reinforcement learning only after negative outcome

PubMed Central

Lee, Daeyeol

2017-01-01

Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the contribution of model-free and model-based reinforcement learning processes differently depending on the valence of outcome, and whether stress alters the learning rate, i.e., how quickly information from the new environment is incorporated into choices. Participants were randomly assigned to either a stress or a control condition, and performed a two-stage Markov decision-making task in which the reward probabilities underwent periodic reversals without notice. We found that stress increased the contribution of model-free reinforcement learning only after negative outcome. Furthermore, stress decreased the learning rate. The results suggest that stress diminishes one’s ability to make adaptive choices in multiple aspects of reinforcement learning. This finding has implications for understanding how stress facilitates maladaptive habits, such as addictive behavior, and other dysfunctional behaviors associated with stress in clinical and educational contexts. PMID:28723943

Stress enhances model-free reinforcement learning only after negative outcome.

PubMed

Park, Heyeon; Lee, Daeyeol; Chey, Jeanyung

2017-01-01

Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the contribution of model-free and model-based reinforcement learning processes differently depending on the valence of outcome, and whether stress alters the learning rate, i.e., how quickly information from the new environment is incorporated into choices. Participants were randomly assigned to either a stress or a control condition, and performed a two-stage Markov decision-making task in which the reward probabilities underwent periodic reversals without notice. We found that stress increased the contribution of model-free reinforcement learning only after negative outcome. Furthermore, stress decreased the learning rate. The results suggest that stress diminishes one's ability to make adaptive choices in multiple aspects of reinforcement learning. This finding has implications for understanding how stress facilitates maladaptive habits, such as addictive behavior, and other dysfunctional behaviors associated with stress in clinical and educational contexts.

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

PubMed Central

Dillon, Laura; Collins, Meaghan; Conway, Maura; Cunningham, Kate

2013-01-01

Three experiments examined the implicit learning of sequences under conditions in which the elements comprising a sequence were equated in terms of reinforcement probability. In Experiment 1 cotton-top tamarins (Saguinus oedipus) experienced a five-element sequence displayed serially on a touch screen in which reinforcement probability was equated across elements at .16 per element. Tamarins demonstrated learning of this sequence with higher latencies during a random test as compared to baseline sequence training. In Experiments 2 and 3, manipulations of the procedure used in the first experiment were undertaken to rule out a confound owing to the fact that the elements in Experiment 1 bore different temporal relations to the intertrial interval (ITI), an inhibitory period. The results of Experiments 2 and 3 indicated that the implicit learning observed in Experiment 1 was not due to temporal proximity between some elements and the inhibitory ITI. The results taken together support two conclusion: First that tamarins engaged in sequence learning whether or not there was contingent reinforcement for learning the sequence, and second that this learning was not due to subtle differences in associative strength between the elements of the sequence. PMID:23344718

Improving the Science Excursion: An Educational Technologist's View

ERIC Educational Resources Information Center

Balson, M.

1973-01-01

Analyzes the nature of the learning process and attempts to show how the three components of a reinforcement contingency, the stimulus, the response and the reinforcement can be utilized to increase the efficiency of a typical science learning experience, the excursion. (JR)

Gaze-contingent reinforcement learning reveals incentive value of social signals in young children and adults

PubMed Central

Smith, Tim J.; Senju, Atsushi

2017-01-01

While numerous studies have demonstrated that infants and adults preferentially orient to social stimuli, it remains unclear as to what drives such preferential orienting. It has been suggested that the learned association between social cues and subsequent reward delivery might shape such social orienting. Using a novel, spontaneous indication of reinforcement learning (with the use of a gaze contingent reward-learning task), we investigated whether children and adults' orienting towards social and non-social visual cues can be elicited by the association between participants' visual attention and a rewarding outcome. Critically, we assessed whether the engaging nature of the social cues influences the process of reinforcement learning. Both children and adults learned to orient more often to the visual cues associated with reward delivery, demonstrating that cue–reward association reinforced visual orienting. More importantly, when the reward-predictive cue was social and engaging, both children and adults learned the cue–reward association faster and more efficiently than when the reward-predictive cue was social but non-engaging. These new findings indicate that social engaging cues have a positive incentive value. This could possibly be because they usually coincide with positive outcomes in real life, which could partly drive the development of social orienting. PMID:28250186

Gaze-contingent reinforcement learning reveals incentive value of social signals in young children and adults.

PubMed

Vernetti, Angélina; Smith, Tim J; Senju, Atsushi

2017-03-15

While numerous studies have demonstrated that infants and adults preferentially orient to social stimuli, it remains unclear as to what drives such preferential orienting. It has been suggested that the learned association between social cues and subsequent reward delivery might shape such social orienting. Using a novel, spontaneous indication of reinforcement learning (with the use of a gaze contingent reward-learning task), we investigated whether children and adults' orienting towards social and non-social visual cues can be elicited by the association between participants' visual attention and a rewarding outcome. Critically, we assessed whether the engaging nature of the social cues influences the process of reinforcement learning. Both children and adults learned to orient more often to the visual cues associated with reward delivery, demonstrating that cue-reward association reinforced visual orienting. More importantly, when the reward-predictive cue was social and engaging, both children and adults learned the cue-reward association faster and more efficiently than when the reward-predictive cue was social but non-engaging. These new findings indicate that social engaging cues have a positive incentive value. This could possibly be because they usually coincide with positive outcomes in real life, which could partly drive the development of social orienting. © 2017 The Authors.

A simple computational algorithm of model-based choice preference.

PubMed

Toyama, Asako; Katahira, Kentaro; Ohira, Hideki

2017-08-01

A broadly used computational framework posits that two learning systems operate in parallel during the learning of choice preferences-namely, the model-free and model-based reinforcement-learning systems. In this study, we examined another possibility, through which model-free learning is the basic system and model-based information is its modulator. Accordingly, we proposed several modified versions of a temporal-difference learning model to explain the choice-learning process. Using the two-stage decision task developed by Daw, Gershman, Seymour, Dayan, and Dolan (2011), we compared their original computational model, which assumes a parallel learning process, and our proposed models, which assume a sequential learning process. Choice data from 23 participants showed a better fit with the proposed models. More specifically, the proposed eligibility adjustment model, which assumes that the environmental model can weight the degree of the eligibility trace, can explain choices better under both model-free and model-based controls and has a simpler computational algorithm than the original model. In addition, the forgetting learning model and its variation, which assume changes in the values of unchosen actions, substantially improved the fits to the data. Overall, we show that a hybrid computational model best fits the data. The parameters used in this model succeed in capturing individual tendencies with respect to both model use in learning and exploration behavior. This computational model provides novel insights into learning with interacting model-free and model-based components.

Reinforcement Learning with Orthonormal Basis Adaptation Based on Activity-Oriented Index Allocation

NASA Astrophysics Data System (ADS)

Satoh, Hideki

An orthonormal basis adaptation method for function approximation was developed and applied to reinforcement learning with multi-dimensional continuous state space. First, a basis used for linear function approximation of a control function is set to an orthonormal basis. Next, basis elements with small activities are replaced with other candidate elements as learning progresses. As this replacement is repeated, the number of basis elements with large activities increases. Example chaos control problems for multiple logistic maps were solved, demonstrating that the method for adapting an orthonormal basis can modify a basis while holding the orthonormality in accordance with changes in the environment to improve the performance of reinforcement learning and to eliminate the adverse effects of redundant noisy states.

A neural model of hierarchical reinforcement learning.

PubMed

Rasmussen, Daniel; Voelker, Aaron; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.

Neural correlates of reinforcement learning and social preferences in competitive bidding.

PubMed

van den Bos, Wouter; Talwar, Arjun; McClure, Samuel M

2013-01-30

In competitive social environments, people often deviate from what rational choice theory prescribes, resulting in losses or suboptimal monetary gains. We investigate how competition affects learning and decision-making in a common value auction task. During the experiment, groups of five human participants were simultaneously scanned using MRI while playing the auction task. We first demonstrate that bidding is well characterized by reinforcement learning with biased reward representations dependent on social preferences. Indicative of reinforcement learning, we found that estimated trial-by-trial prediction errors correlated with activity in the striatum and ventromedial prefrontal cortex. Additionally, we found that individual differences in social preferences were related to activity in the temporal-parietal junction and anterior insula. Connectivity analyses suggest that monetary and social value signals are integrated in the ventromedial prefrontal cortex and striatum. Based on these results, we argue for a novel mechanistic account for the integration of reinforcement history and social preferences in competitive decision-making.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

NASA Technical Reports Server (NTRS)

Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

2013-01-01

Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

Extinction of Pavlovian conditioning: The influence of trial number and reinforcement history.

PubMed

Chan, C K J; Harris, Justin A

2017-08-01

Pavlovian conditioning is sensitive to the temporal relationship between the conditioned stimulus (CS) and the unconditioned stimulus (US). This has motivated models that describe learning as a process that continuously updates associative strength during the trial or specifically encodes the CS-US interval. These models predict that extinction of responding is also continuous, such that response loss is proportional to the cumulative duration of exposure to the CS without the US. We review evidence showing that this prediction is incorrect, and that extinction is trial-based rather than time-based. We also present two experiments that test the importance of trials versus time on the Partial Reinforcement Extinction Effect (PREE), in which responding extinguishes more slowly for a CS that was inconsistently reinforced with the US than for a consistently reinforced one. We show that increasing the number of extinction trials of the partially reinforced CS, relative to the consistently reinforced CS, overcomes the PREE. However, increasing the duration of extinction trials by the same amount does not overcome the PREE. We conclude that animals learn about the likelihood of the US per trial during conditioning, and learn trial-by-trial about the absence of the US during extinction. Moreover, what they learn about the likelihood of the US during conditioning affects how sensitive they are to the absence of the US during extinction. Copyright © 2017 Elsevier B.V. All rights reserved.

Can Service Learning Reinforce Social and Cultural Bias? Exploring a Popular Model of Family Involvement for Early Childhood Teacher Candidates

ERIC Educational Resources Information Center

Dunn-Kenney, Maylan

2010-01-01

Service learning is often used in teacher education as a way to challenge social bias and provide teacher candidates with skills needed to work in partnership with diverse families. Although some literature suggests that service learning could reinforce cultural bias, there is little documentation. In a study of 21 early childhood teacher…

Deep Gate Recurrent Neural Network

DTIC Science & Technology

2016-11-22

Schmidhuber. A system for robotic heart surgery that learns to tie knots using recurrent neural networks. In IEEE International Conference on...tasks, such as Machine Translation (Bahdanau et al. (2015)) or Robot Reinforcement Learning (Bakker (2001)). The main idea behind these networks is to...and J. Peters. Reinforcement learning in robotics : A survey. The International Journal of Robotics Research, 32:1238–1274, 2013. ISSN 0278-3649. doi

Conceptualizing withdrawal-induced escalation of alcohol self-administration as a learned, plasticity-dependent process

PubMed Central

Walker, Brendan M.

2013-01-01

This article represents one of five contributions focusing on the topic “Plasticity and neuroadaptive responses within the extended amygdala in response to chronic or excessive alcohol exposure” that were developed by awardees participating in the Young Investigator Award Symposium at the “Alcoholism and Stress: A Framework for Future Treatment Strategies” conference in Volterra, Italy on May 3–6, 2011 that was organized/chaired by Drs. Antonio Noronha and Fulton Crews and sponsored by the National Institute on Alcohol Abuse and Alcoholism. This review discusses the dependence-induced neuroadaptations in affective systems that provide a basis for negative reinforcement learning and presents evidence demonstrating that escalated alcohol consumption during withdrawal is a learned, plasticity-dependent process. The review concludes by identifying changes within extended amygdala dynorphin/kappa-opioid receptor systems that could serve as the foundation for the occurrence of negative reinforcement processes. While some evidence contained herein may be specific to alcohol dependence-related learning and plasticity, much of the information will be of relevance to any addictive disorder involving negative reinforcement mechanisms. Collectively, the information presented within this review provides a framework to assess the negative reinforcing effects of alcohol in a manner that distinguishes neuroadaptations produced by chronic alcohol exposure from the actual plasticity that is associated with negative reinforcement learning in dependent organisms. PMID:22459874

Reinforcement Learning Strategies for Clinical Trials in Non-small Cell Lung Cancer

PubMed Central

Zhao, Yufan; Zeng, Donglin; Socinski, Mark A.; Kosorok, Michael R.

2010-01-01

Summary Typical regimens for advanced metastatic stage IIIB/IV non-small cell lung cancer (NSCLC) consist of multiple lines of treatment. We present an adaptive reinforcement learning approach to discover optimal individualized treatment regimens from a specially designed clinical trial (a “clinical reinforcement trial”) of an experimental treatment for patients with advanced NSCLC who have not been treated previously with systemic therapy. In addition to the complexity of the problem of selecting optimal compounds for first and second-line treatments based on prognostic factors, another primary goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. A reinforcement learning method called Q-learning is utilized which involves learning an optimal regimen from patient data generated from the clinical reinforcement trial. Approximating the Q-function with time-indexed parameters can be achieved by using a modification of support vector regression which can utilize censored data. Within this framework, a simulation study shows that the procedure can extract optimal regimens for two lines of treatment directly from clinical data without prior knowledge of the treatment effect mechanism. In addition, we demonstrate that the design reliably selects the best initial time for second-line therapy while taking into account the heterogeneity of NSCLC across patients. PMID:21385164

How to Demonstrate Microgravity in your Classroom

NASA Technical Reports Server (NTRS)

DeLombard, Richard; Hall, Nancy Rabel

2013-01-01

Learn why zero gravity is a misnomer and learn how to demonstrate microgravity to students and the general public. In this session, a short theory segment will explain and reinforce these concepts so that you may explain to others. Session participants will also see simple equipment that demonstrates microgravity during the session and can just as well be done in the classroom or museum exhibit hall. The hands-on demonstration devices range from a leaky water bottle to an electronic drop tower with an on-board camera. The session will also include demonstration techniques for Physics, Forces & Motion, and orbits. This material is useful for middle school forces and motions instruction, high school physics instruction, public demonstrations at conferences & school open houses, travelling museum exhibits, fixed museum exhibits, and independent student projects or experiments. These activities also connect the terrestrial demonstration with planetary & moon motion, comet trajectory, and more.

Tracked robot controllers for climbing obstacles autonomously

NASA Astrophysics Data System (ADS)

Vincent, Isabelle

2009-05-01

Research in mobile robot navigation has demonstrated some success in navigating flat indoor environments while avoiding obstacles. However, the challenge of analyzing complex environments to climb obstacles autonomously has had very little success due to the complexity of the task. Unmanned ground vehicles currently exhibit simple autonomous behaviours compared to the human ability to move in the world. This paper presents the control algorithms designed for a tracked mobile robot to autonomously climb obstacles by varying its tracks configuration. Two control algorithms are proposed to solve the autonomous locomotion problem for climbing obstacles. First, a reactive controller evaluates the appropriate geometric configuration based on terrain and vehicle geometric considerations. Then, a reinforcement learning algorithm finds alternative solutions when the reactive controller gets stuck while climbing an obstacle. The methodology combines reactivity to learning. The controllers have been demonstrated in box and stair climbing simulations. The experiments illustrate the effectiveness of the proposed approach for crossing obstacles.

Action Centered Contextual Bandits.

PubMed

Greenewald, Kristjan; Tewari, Ambuj; Klasnja, Predrag; Murphy, Susan

2017-12-01

Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees. Unfortunately, in emerging applications in mobile health, the time-invariant linear model assumption is untenable. We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications. At the same time, it leads to algorithms with strong performance guarantees as in the linear model setting, while still allowing for complex nonlinear baseline modeling. Our theory is supported by experiments on data gathered in a recently concluded mobile health study.

Depression, Activity, and Evaluation of Reinforcement

ERIC Educational Resources Information Center

Hammen, Constance L.; Glass, David R., Jr.

1975-01-01

This research attempted to find the causal relation between mood and level of reinforcement. An effort was made to learn what mood change might occur if depressed subjects increased their levels of participation in reinforcing activities. (Author/RK)

What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics

NASA Astrophysics Data System (ADS)

Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

Kinesthetic Reinforcement-Is It a Boon to Learning?

ERIC Educational Resources Information Center

Bohrer, Roxilu K.

1970-01-01

Language instruction, particularly in the elementary school, should be reinforced through the use of visual aids and through associated physical activity. Kinesthetic experiences provide an opportunity to make use of non-verbal cues to meaning, enliven classroom activities, and maximize learning for pupils. The author discusses the educational…

Reinforcing Basic Skills Through Social Studies. Grades 4-7.

ERIC Educational Resources Information Center

Lewis, Teresa Marie

Arranged into seven parts, this document provides a variety of games and activities, bulletin board ideas, overhead transparencies, student handouts, and learning station ideas to help reinforce basic social studies skills in the intermediate grades. In part 1, students learn about timelines, first constructing their own life timeline, then a…

Effects of Reinforcement on Peer Imitation in a Small Group Play Context

ERIC Educational Resources Information Center

Barton, Erin E.; Ledford, Jennifer R.

2018-01-01

Children with disabilities often have deficits in imitation skills, particularly in imitating peers. Imitation is considered a behavioral cusp--which, once learned, allows a child to access additional and previously unavailable learning opportunities. In the current study, researchers examined the efficacy of contingent reinforcement delivered…

Neurofeedback in Learning Disabled Children: Visual versus Auditory Reinforcement.

PubMed

Fernández, Thalía; Bosch-Bayard, Jorge; Harmony, Thalía; Caballero, María I; Díaz-Comas, Lourdes; Galán, Lídice; Ricardo-Garcell, Josefina; Aubert, Eduardo; Otero-Ojeda, Gloria

2016-03-01

Children with learning disabilities (LD) frequently have an EEG characterized by an excess of theta and a deficit of alpha activities. NFB using an auditory stimulus as reinforcer has proven to be a useful tool to treat LD children by positively reinforcing decreases of the theta/alpha ratio. The aim of the present study was to optimize the NFB procedure by comparing the efficacy of visual (with eyes open) versus auditory (with eyes closed) reinforcers. Twenty LD children with an abnormally high theta/alpha ratio were randomly assigned to the Auditory or the Visual group, where a 500 Hz tone or a visual stimulus (a white square), respectively, was used as a positive reinforcer when the value of the theta/alpha ratio was reduced. Both groups had signs consistent with EEG maturation, but only the Auditory Group showed behavioral/cognitive improvements. In conclusion, the auditory reinforcer was more efficacious in reducing the theta/alpha ratio, and it improved the cognitive abilities more than the visual reinforcer.

Behavior systems and reinforcement: an integrative approach.

PubMed Central

Timberlake, W

1993-01-01

Most traditional conceptions of reinforcement are based on a simple causal model in which responding is strengthened by the presentation of a reinforcer. I argue that reinforcement is better viewed as the outcome of constraint of a functioning causal system comprised of multiple interrelated causal sequences, complex linkages between causes and effects, and a set of initial conditions. Using a simplified system conception of the reinforcement situation, I review the similarities and drawbacks of traditional reinforcement models and analyze the recent contributions of cognitive, regulatory, and ecological approaches. Finally, I show how the concept of behavior systems can begin to incorporate both traditional and recent conceptions of reinforcement in an integrative approach. PMID:8354963

Reinforcement learning agents providing advice in complex video games

NASA Astrophysics Data System (ADS)

Taylor, Matthew E.; Carboni, Nicholas; Fachantidis, Anestis; Vlahavas, Ioannis; Torrey, Lisa

2014-01-01

This article introduces a teacher-student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

The probability of reinforcement per trial affects posttrial responding and subsequent extinction but not within-trial responding.

PubMed

Harris, Justin A; Kwok, Dorothy W S

2018-01-01

During magazine approach conditioning, rats do not discriminate between a conditional stimulus (CS) that is consistently reinforced with food and a CS that is occasionally (partially) reinforced, as long as the CSs have the same overall reinforcement rate per second. This implies that rats are indifferent to the probability of reinforcement per trial. However, in the same rats, the per-trial reinforcement rate will affect subsequent extinction-responding extinguishes more rapidly for a CS that was consistently reinforced than for a partially reinforced CS. Here, we trained rats with consistently and partially reinforced CSs that were matched for overall reinforcement rate per second. We measured conditioned responding both during and immediately after the CSs. Differences in the per-trial probability of reinforcement did not affect the acquisition of responding during the CS but did affect subsequent extinction of that responding, and also affected the post-CS response rates during conditioning. Indeed, CSs with the same probability of reinforcement per trial evoked the same amount of post-CS responding even when they differed in overall reinforcement rate and thus evoked different amounts of responding during the CS. We conclude that reinforcement rate per second controls rats' acquisition of responding during the CS, but at the same time, rats also learn specifically about the probability of reinforcement per trial. The latter learning affects the rats' expectation of reinforcement as an outcome of the trial, which influences their ability to detect retrospectively that an opportunity for reinforcement was missed, and, in turn, drives extinction. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Establishment and Maintenance of Socially Learned Conditioned Reinforcement in Young Children: Elimination of the Role of Adults and View of Peers' Faces

ERIC Educational Resources Information Center

Zrinzo, Michelle; Greer, R. Douglas

2013-01-01

Prior research has demonstrated the establishment of reinforcers for learning and maintenance with young children as a function of social learning where a peer and an adult experimenter were present. The presence of an adult experimenter was eliminated in the present study to test if the effect produced in the prior studies would occur with only…

Structure identification in fuzzy inference using reinforcement learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1993-01-01

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Spared internal but impaired external reward prediction error signals in major depressive disorder during reinforcement learning.

PubMed

Bakic, Jasmina; Pourtois, Gilles; Jepma, Marieke; Duprat, Romain; De Raedt, Rudi; Baeken, Chris

2017-01-01

Major depressive disorder (MDD) creates debilitating effects on a wide range of cognitive functions, including reinforcement learning (RL). In this study, we sought to assess whether reward processing as such, or alternatively the complex interplay between motivation and reward might potentially account for the abnormal reward-based learning in MDD. A total of 35 treatment resistant MDD patients and 44 age matched healthy controls (HCs) performed a standard probabilistic learning task. RL was titrated using behavioral, computational modeling and event-related brain potentials (ERPs) data. MDD patients showed comparable learning rate compared to HCs. However, they showed decreased lose-shift responses as well as blunted subjective evaluations of the reinforcers used during the task, relative to HCs. Moreover, MDD patients showed normal internal (at the level of error-related negativity, ERN) but abnormal external (at the level of feedback-related negativity, FRN) reward prediction error (RPE) signals during RL, selectively when additional efforts had to be made to establish learning. Collectively, these results lend support to the assumption that MDD does not impair reward processing per se during RL. Instead, it seems to alter the processing of the emotional value of (external) reinforcers during RL, when additional intrinsic motivational processes have to be engaged. © 2016 Wiley Periodicals, Inc.

Relationship between Reinforcement and Eye Movements during Ocular Motor Training with Learning Disabled Children.

ERIC Educational Resources Information Center

Punnett, Audrey F.; Steinhauer, Gene D.

1984-01-01

Four reading disabled children were given eight sessions of ocular motor training with reinforcement and eight sessions without reinforcement. Two reading disabled control Ss were treated similarly but received no ocular motor training. Results demonstrated that reinforcement can improve ocular motor skills, which in turn elevates reading…

The partial-reinforcement extinction effect and the contingent-sampling hypothesis.

PubMed

Hochman, Guy; Erev, Ido

2013-12-01

The partial-reinforcement extinction effect (PREE) implies that learning under partial reinforcements is more robust than learning under full reinforcements. While the advantages of partial reinforcements have been well-documented in laboratory studies, field research has failed to support this prediction. In the present study, we aimed to clarify this pattern. Experiment 1 showed that partial reinforcements increase the tendency to select the promoted option during extinction; however, this effect is much smaller than the negative effect of partial reinforcements on the tendency to select the promoted option during the training phase. Experiment 2 demonstrated that the overall effect of partial reinforcements varies inversely with the attractiveness of the alternative to the promoted behavior: The overall effect is negative when the alternative is relatively attractive, and positive when the alternative is relatively unattractive. These results can be captured with a contingent-sampling model assuming that people select options that provided the best payoff in similar past experiences. The best fit was obtained under the assumption that similarity is defined by the sequence of the last four outcomes.

Design equations for the assessment and FRP-strengthening of reinforced rectangular concrete columns under combined biaxial bending and axial loads

NASA Astrophysics Data System (ADS)

Alessandri, S.; Monti, G.

2008-05-01

A simple procedure is proposed for the assessment of reinforced rectangular concrete columns under combined biaxial bending and axial loads and for the design of a correct amount of FRP-strengthening for underdesigned concrete sections. Approximate closed-form equations are developed based on the load contour method originally proposed by Bresler for reinforced concrete sections. The 3D failure surface is approximated along its contours, at a constant axial load, by means of equations given as the sum of the acting/resisting moment ratio in the directions of principal axes of the sections, raised to a power depending on the axial load, the steel reinforcement ratio, and the section shape. The method is extended to FRP-strengthened sections. Moreover, to make it possible to apply the load contour method in a more practical way, simple closed-form equations are developed for rectangular reinforced concrete sections with a two-way steel reinforcement and FRP strengthenings on each side. A comparison between the approach proposed and the fiber method (which is considered exact) shows that the simplified equations correctly represent the section interaction diagram.

The effects of aging on the interaction between reinforcement learning and attention.

PubMed

Radulescu, Angela; Daniel, Reka; Niv, Yael

2016-11-01

Reinforcement learning (RL) in complex environments relies on selective attention to uncover those aspects of the environment that are most predictive of reward. Whereas previous work has focused on age-related changes in RL, it is not known whether older adults learn differently from younger adults when selective attention is required. In 2 experiments, we examined how aging affects the interaction between RL and selective attention. Younger and older adults performed a learning task in which only 1 stimulus dimension was relevant to predicting reward, and within it, 1 "target" feature was the most rewarding. Participants had to discover this target feature through trial and error. In Experiment 1, stimuli varied on 1 or 3 dimensions and participants received hints that revealed the target feature, the relevant dimension, or gave no information. Group-related differences in accuracy and RTs differed systematically as a function of the number of dimensions and the type of hint available. In Experiment 2 we used trial-by-trial computational modeling of the learning process to test for age-related differences in learning strategies. Behavior of both young and older adults was explained well by a reinforcement-learning model that uses selective attention to constrain learning. However, the model suggested that older adults restricted their learning to fewer features, employing more focused attention than younger adults. Furthermore, this difference in strategy predicted age-related deficits in accuracy. We discuss these results suggesting that a narrower filter of attention may reflect an adaptation to the reduced capabilities of the reinforcement learning system. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Tiger salamanders' (Ambystoma tigrinum) response learning and usage of visual cues.

PubMed

Kundey, Shannon M A; Millar, Roberto; McPherson, Justin; Gonzalez, Maya; Fitz, Aleyna; Allen, Chadbourne

2016-05-01

We explored tiger salamanders' (Ambystoma tigrinum) learning to execute a response within a maze as proximal visual cue conditions varied. In Experiment 1, salamanders learned to turn consistently in a T-maze for reinforcement before the maze was rotated. All learned the initial task and executed the trained turn during test, suggesting that they learned to demonstrate the reinforced response during training and continued to perform it during test. In a second experiment utilizing a similar procedure, two visual cues were placed consistently at the maze junction. Salamanders were reinforced for turning towards one cue. Cue placement was reversed during test. All learned the initial task, but executed the trained turn rather than turning towards the visual cue during test, evidencing response learning. In Experiment 3, we investigated whether a compound visual cue could control salamanders' behaviour when it was the only cue predictive of reinforcement in a cross-maze by varying start position and cue placement. All learned to turn in the direction indicated by the compound visual cue, indicating that visual cues can come to control their behaviour. Following training, testing revealed that salamanders attended to stimuli foreground over background features. Overall, these results suggest that salamanders learn to execute responses over learning to use visual cues but can use visual cues if required. Our success with this paradigm offers the potential in future studies to explore salamanders' cognition further, as well as to shed light on how features of the tiger salamanders' life history (e.g. hibernation and metamorphosis) impact cognition.

Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.

PubMed

Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla

2014-12-01

This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies.

Refining Linear Fuzzy Rules by Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap S.; Malkani, Anil

1996-01-01

Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.

Instructed knowledge shapes feedback-driven aversive learning in striatum and orbitofrontal cortex, but not the amygdala

PubMed Central

Atlas, Lauren Y; Doll, Bradley B; Li, Jian; Daw, Nathaniel D; Phelps, Elizabeth A

2016-01-01

Socially-conveyed rules and instructions strongly shape expectations and emotions. Yet most neuroscientific studies of learning consider reinforcement history alone, irrespective of knowledge acquired through other means. We examined fear conditioning and reversal in humans to test whether instructed knowledge modulates the neural mechanisms of feedback-driven learning. One group was informed about contingencies and reversals. A second group learned only from reinforcement. We combined quantitative models with functional magnetic resonance imaging and found that instructions induced dissociations in the neural systems of aversive learning. Responses in striatum and orbitofrontal cortex updated with instructions and correlated with prefrontal responses to instructions. Amygdala responses were influenced by reinforcement similarly in both groups and did not update with instructions. Results extend work on instructed reward learning and reveal novel dissociations that have not been observed with punishments or rewards. Findings support theories of specialized threat-detection and may have implications for fear maintenance in anxiety. DOI: http://dx.doi.org/10.7554/eLife.15192.001 PMID:27171199

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Evaluation of corrosion resistance of various concrete reinforcing materials.

DOT National Transportation Integrated Search

2013-06-01

The Vermont Agency of Transportation undertook a simple experiment to determine the corrosion : resistance ability of various reinforcing steels (rebar) that may be used in bridges and other concrete : structures. Eight types of rebar were used in th...

Post-learning infusion of anisomycin into the anterior cingulate cortex impairs instrumental acquisition through an effect on reinforcer valuation

PubMed Central

Jonkman, Sietse; Everitt, Barry J.

2009-01-01

The integrity of the rodent anterior cingulate cortex (ACC) is essential for various aspects of instrumental behavior, but it is not clear if the ACC is important for the acquisition of a simple instrumental response. Here, it was demonstrated that post-session infusions of anisomycin into the rat ACC completely prevented the acquisition of instrumental responding. The experimental use of post-session intracranial infusions of plasticity inhibitors is assumed to affect local consolidation of plasticity, but not behavioral task performance. However, in associative appetitive conditioning, post-session intracranial infusion of pharmaco-active compounds could actually interfere with subsequent task performance indirectly through retrospective effects on the valuation of ingested rewards. Thus, it was subsequently demonstrated that the intracranial infusion of anisomycin into the ACC after sucrose pellet consumption significantly reduced subsequent pellet consumption, suggesting that the infusion of anisomycin into the ACC produced conditioned taste avoidance. In the third experiment, an innovative procedure was introduced that dissociated the effects of intracranial infusions after conditioning sessions on task-learning and unconditioned stimulus valuation. With this procedure, the infusion of anisomycin into the ACC after instrumental sessions did not affect instrumental reinforcer valuation or the acquisition of instrumental responding, suggesting that plasticity in the ACC is not necessary for the acquisition of instrumental behavior. PMID:19864297

Advances and limitations of visual conditioning protocols in harnessed bees.

PubMed

Avarguès-Weber, Aurore; Mota, Theo

2016-10-01

Bees are excellent invertebrate models for studying visual learning and memory mechanisms, because of their sophisticated visual system and impressive cognitive capacities associated with a relatively simple brain. Visual learning in free-flying bees has been traditionally studied using an operant conditioning paradigm. This well-established protocol, however, can hardly be combined with invasive procedures for studying the neurobiological basis of visual learning. Different efforts have been made to develop protocols in which harnessed honey bees could associate visual cues with reinforcement, though learning performances remain poorer than those obtained with free-flying animals. Especially in the last decade, the intention of improving visual learning performances of harnessed bees led many authors to adopt distinct visual conditioning protocols, altering parameters like harnessing method, nature and duration of visual stimulation, number of trials, inter-trial intervals, among others. As a result, the literature provides data hardly comparable and sometimes contradictory. In the present review, we provide an extensive analysis of the literature available on visual conditioning of harnessed bees, with special emphasis on the comparison of diverse conditioning parameters adopted by different authors. Together with this comparative overview, we discuss how these diverse conditioning parameters could modulate visual learning performances of harnessed bees. Copyright © 2016 Elsevier Ltd. All rights reserved.

Life Span Differences in Electrophysiological Correlates of Monitoring Gains and Losses during Probabilistic Reinforcement Learning

ERIC Educational Resources Information Center

Hammerer, Dorothea; Li, Shu-Chen; Muller, Viktor; Lindenberger, Ulman

2011-01-01

By recording the feedback-related negativity (FRN) in response to gains and losses, we investigated the contribution of outcome monitoring mechanisms to age-associated differences in probabilistic reinforcement learning. Specifically, we assessed the difference of the monitoring reactions to gains and losses to investigate the monitoring of…

Reinforcement Learning in Young Adults with Developmental Language Impairment

ERIC Educational Resources Information Center

Lee, Joanna C.; Tomblin, J. Bruce

2012-01-01

The aim of the study was to examine reinforcement learning (RL) in young adults with developmental language impairment (DLI) within the context of a neurocomputational model of the basal ganglia-dopamine system (Frank, Seeberger, & O'Reilly, 2004). Two groups of young adults, one with DLI and the other without, were recruited. A probabilistic…

Effective Reinforcement Techniques in Elementary Physical Education: The Key to Behavior Management

ERIC Educational Resources Information Center

Downing, John; Keating, Tedd; Bennett, Carl

2005-01-01

The ability to shape appropriate behavior while extinguishing misbehavior is critical to teaching and learning in physical education. The scientific principles that affect student learning in the gymnasium also apply to the methods teachers use to influence social behaviors. Research indicates that reinforcement strategies are more effective than…

A neural model of hierarchical reinforcement learning

PubMed Central

Rasmussen, Daniel; Eliasmith, Chris

2017-01-01

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain’s general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model’s behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions. PMID:28683111

Reinforcement Learning Trees

PubMed Central

Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.

2015-01-01

In this paper, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman, 2001) under high-dimensional settings. The innovations are three-fold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later splits, rather than choosing the one with largest marginal effect from the immediate split, the constructed tree utilizes the available samples in a more efficient way. Moreover, such an approach enables linear combination cuts at little extra computational cost. Second, we propose a variable muting procedure that progressively eliminates noise variables during the construction of each individual tree. The muting procedure also takes advantage of reinforcement learning and prevents noise variables from being considered in the search for splitting rules, so that towards terminal nodes, where the sample size is small, the splitting rules are still constructed from only strong variables. Last, we investigate asymptotic properties of the proposed method under basic assumptions and discuss rationale in general settings. PMID:26903687

Reinforcement learning state estimator.

PubMed

Morimoto, Jun; Doya, Kenji

2007-03-01

In this study, we propose a novel use of reinforcement learning for estimating hidden variables and parameters of nonlinear dynamical systems. A critical issue in hidden-state estimation is that we cannot directly observe estimation errors. However, by defining errors of observable variables as a delayed penalty, we can apply a reinforcement learning frame-work to state estimation problems. Specifically, we derive a method to construct a nonlinear state estimator by finding an appropriate feedback input gain using the policy gradient method. We tested the proposed method on single pendulum dynamics and show that the joint angle variable could be successfully estimated by observing only the angular velocity, and vice versa. In addition, we show that we could acquire a state estimator for the pendulum swing-up task in which a swing-up controller is also acquired by reinforcement learning simultaneously. Furthermore, we demonstrate that it is possible to estimate the dynamics of the pendulum itself while the hidden variables are estimated in the pendulum swing-up task. Application of the proposed method to a two-linked biped model is also presented.

Reciprocity Family Counseling: A Multi-Ethnic Model.

ERIC Educational Resources Information Center

Penrose, David M.

The Reciprocity Family Counseling Method involves learning principles of behavior modification including selective reinforcement, behavioral contracting, self-correction, and over-correction. Selective reinforcement refers to the recognition and modification of parent/child responses and reinforcers. Parents and children are asked to identify…

Reinforcement active learning in the vibrissae system: optimal object localization.

PubMed

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

An intelligent agent for optimal river-reservoir system management

NASA Astrophysics Data System (ADS)

Rieker, Jeffrey D.; Labadie, John W.

2012-09-01

A generalized software package is presented for developing an intelligent agent for stochastic optimization of complex river-reservoir system management and operations. Reinforcement learning is an approach to artificial intelligence for developing a decision-making agent that learns the best operational policies without the need for explicit probabilistic models of hydrologic system behavior. The agent learns these strategies experientially in a Markov decision process through observational interaction with the environment and simulation of the river-reservoir system using well-calibrated models. The graphical user interface for the reinforcement learning process controller includes numerous learning method options and dynamic displays for visualizing the adaptive behavior of the agent. As a case study, the generalized reinforcement learning software is applied to developing an intelligent agent for optimal management of water stored in the Truckee river-reservoir system of California and Nevada for the purpose of streamflow augmentation for water quality enhancement. The intelligent agent successfully learns long-term reservoir operational policies that specifically focus on mitigating water temperature extremes during persistent drought periods that jeopardize the survival of threatened and endangered fish species.

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

PubMed

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

A comparison of differential reinforcement procedures with children with autism.

PubMed

Boudreau, Brittany A; Vladescu, Jason C; Kodak, Tiffany M; Argott, Paul J; Kisamore, April N

2015-12-01

The current evaluation compared the effects of 2 differential reinforcement arrangements and a nondifferential reinforcement arrangement on the acquisition of tacts for 3 children with autism. Participants learned in all reinforcement-based conditions, and we discuss areas for future research in light of these findings and potential limitations. © Society for the Experimental Analysis of Behavior.

Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis

PubMed Central

2013-01-01

Background Depression is characterised partly by blunted reactions to reward. However, tasks probing this deficiency have not distinguished insensitivity to reward from insensitivity to the prediction errors for reward that determine learning and are putatively reported by the phasic activity of dopamine neurons. We attempted to disentangle these factors with respect to anhedonia in the context of stress, Major Depressive Disorder (MDD), Bipolar Disorder (BPD) and a dopaminergic challenge. Methods Six behavioural datasets involving 392 experimental sessions were subjected to a model-based, Bayesian meta-analysis. Participants across all six studies performed a probabilistic reward task that used an asymmetric reinforcement schedule to assess reward learning. Healthy controls were tested under baseline conditions, stress or after receiving the dopamine D2 agonist pramipexole. In addition, participants with current or past MDD or BPD were evaluated. Reinforcement learning models isolated the contributions of variation in reward sensitivity and learning rate. Results MDD and anhedonia reduced reward sensitivity more than they affected the learning rate, while a low dose of the dopamine D2 agonist pramipexole showed the opposite pattern. Stress led to a pattern consistent with a mixed effect on reward sensitivity and learning rate. Conclusion Reward-related learning reflected at least two partially separable contributions. The first related to phasic prediction error signalling, and was preferentially modulated by a low dose of the dopamine agonist pramipexole. The second related directly to reward sensitivity, and was preferentially reduced in MDD and anhedonia. Stress altered both components. Collectively, these findings highlight the contribution of model-based reinforcement learning meta-analysis for dissecting anhedonic behavior. PMID:23782813

Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

PubMed

Ezaki, Takahiro; Horita, Yutaka; Takezawa, Masanori; Masuda, Naoki

2016-07-01

Direct reciprocity, or repeated interaction, is a main mechanism to sustain cooperation under social dilemmas involving two individuals. For larger groups and networks, which are probably more relevant to understanding and engineering our society, experiments employing repeated multiplayer social dilemma games have suggested that humans often show conditional cooperation behavior and its moody variant. Mechanisms underlying these behaviors largely remain unclear. Here we provide a proximate account for this behavior by showing that individuals adopting a type of reinforcement learning, called aspiration learning, phenomenologically behave as conditional cooperator. By definition, individuals are satisfied if and only if the obtained payoff is larger than a fixed aspiration level. They reinforce actions that have resulted in satisfactory outcomes and anti-reinforce those yielding unsatisfactory outcomes. The results obtained in the present study are general in that they explain extant experimental results obtained for both so-called moody and non-moody conditional cooperation, prisoner's dilemma and public goods games, and well-mixed groups and networks. Different from the previous theory, individuals are assumed to have no access to information about what other individuals are doing such that they cannot explicitly use conditional cooperation rules. In this sense, myopic aspiration learning in which the unconditional propensity of cooperation is modulated in every discrete time step explains conditional behavior of humans. Aspiration learners showing (moody) conditional cooperation obeyed a noisy GRIM-like strategy. This is different from the Pavlov, a reinforcement learning strategy promoting mutual cooperation in two-player situations.

Social stress reactivity alters reward and punishment learning

PubMed Central

Frank, Michael J.; Allen, John J. B.

2011-01-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems. PMID:20453038

Social stress reactivity alters reward and punishment learning.

PubMed

Cavanagh, James F; Frank, Michael J; Allen, John J B

2011-06-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems.

Predicting psychosis across diagnostic boundaries: Behavioral and computational modeling evidence for impaired reinforcement learning in schizophrenia and bipolar disorder with a history of psychosis.

PubMed

Strauss, Gregory P; Thaler, Nicholas S; Matveeva, Tatyana M; Vogel, Sally J; Sutton, Griffin P; Lee, Bern G; Allen, Daniel N

2015-08-01

There is increasing evidence that schizophrenia (SZ) and bipolar disorder (BD) share a number of cognitive, neurobiological, and genetic markers. Shared features may be most prevalent among SZ and BD with a history of psychosis. This study extended this literature by examining reinforcement learning (RL) performance in individuals with SZ (n = 29), BD with a history of psychosis (BD+; n = 24), BD without a history of psychosis (BD-; n = 23), and healthy controls (HC; n = 24). RL was assessed through a probabilistic stimulus selection task with acquisition and test phases. Computational modeling evaluated competing accounts of the data. Each participant's trial-by-trial decision-making behavior was fit to 3 computational models of RL: (a) a standard actor-critic model simulating pure basal ganglia-dependent learning, (b) a pure Q-learning model simulating action selection as a function of learned expected reward value, and (c) a hybrid model where an actor-critic is "augmented" by a Q-learning component, meant to capture the top-down influence of orbitofrontal cortex value representations on the striatum. The SZ group demonstrated greater reinforcement learning impairments at acquisition and test phases than the BD+, BD-, and HC groups. The BD+ and BD- groups displayed comparable performance at acquisition and test phases. Collapsing across diagnostic categories, greater severity of current psychosis was associated with poorer acquisition of the most rewarding stimuli as well as poor go/no-go learning at test. Model fits revealed that reinforcement learning in SZ was best characterized by a pure actor-critic model where learning is driven by prediction error signaling alone. In contrast, BD-, BD+, and HC were best fit by a hybrid model where prediction errors are influenced by top-down expected value representations that guide decision making. These findings suggest that abnormalities in the reward system are more prominent in SZ than BD; however, current psychotic symptoms may be associated with reinforcement learning deficits regardless of a Diagnostic and Statistical Manual of Mental Disorders (5th Edition; American Psychiatric Association, 2013) diagnosis. (c) 2015 APA, all rights reserved).

Motor Learning Enhances Use-Dependent Plasticity

PubMed Central

2017-01-01

Motor behaviors are shaped not only by current sensory signals but also by the history of recent experiences. For instance, repeated movements toward a particular target bias the subsequent movements toward that target direction. This process, called use-dependent plasticity (UDP), is considered a basic and goal-independent way of forming motor memories. Most studies consider movement history as the critical component that leads to UDP (Classen et al., 1998; Verstynen and Sabes, 2011). However, the effects of learning (i.e., improved performance) on UDP during movement repetition have not been investigated. Here, we used transcranial magnetic stimulation in two experiments to assess plasticity changes occurring in the primary motor cortex after individuals repeated reinforced and nonreinforced actions. The first experiment assessed whether learning a skill task modulates UDP. We found that a group that successfully learned the skill task showed greater UDP than a group that did not accumulate learning, but made comparable repeated actions. The second experiment aimed to understand the role of reinforcement learning in UDP while controlling for reward magnitude and action kinematics. We found that providing subjects with a binary reward without visual feedback of the cursor led to increased UDP effects. Subjects in the group that received comparable reward not associated with their actions maintained the previously induced UDP. Our findings illustrate how reinforcing consistent actions strengthens use-dependent memories and provide insight into operant mechanisms that modulate plastic changes in the motor cortex. SIGNIFICANCE STATEMENT Performing consistent motor actions induces use-dependent plastic changes in the motor cortex. This plasticity reflects one of the basic forms of human motor learning. Past studies assumed that this form of learning is exclusively affected by repetition of actions. However, here we showed that success-based reinforcement signals could affect the human use-dependent plasticity (UDP) process. Our results indicate that learning augments and interacts with UDP. This effect is important to the understanding of the interplay between the different forms of motor learning and suggests that reinforcement is not only important to learning new behaviors, but can shape our subsequent behavior via its interaction with UDP. PMID:28143961

Toward a dual-learning systems model of speech category learning

PubMed Central

Chandrasekaran, Bharath; Koslov, Seth R.; Maddox, W. T.

2014-01-01

More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions. PMID:25132827

Specific effect of a dopamine partial agonist on counterfactual learning: evidence from Gilles de la Tourette syndrome.

PubMed

Salvador, Alexandre; Worbe, Yulia; Delorme, Cécile; Coricelli, Giorgio; Gaillard, Raphaël; Robbins, Trevor W; Hartmann, Andreas; Palminteri, Stefano

2017-07-24

The dopamine partial agonist aripiprazole is increasingly used to treat pathologies for which other antipsychotics are indicated because it displays fewer side effects, such as sedation and depression-like symptoms, than other dopamine receptor antagonists. Previously, we showed that aripiprazole may protect motivational function by preserving reinforcement-related signals used to sustain reward-maximization. However, the effect of aripiprazole on more cognitive facets of human reinforcement learning, such as learning from the forgone outcomes of alternative courses of action (i.e., counterfactual learning), is unknown. To test the influence of aripiprazole on counterfactual learning, we administered a reinforcement learning task that involves both direct learning from obtained outcomes and indirect learning from forgone outcomes to two groups of Gilles de la Tourette (GTS) patients, one consisting of patients who were completely unmedicated and the other consisting of patients who were receiving aripiprazole monotherapy, and to healthy subjects. We found that whereas learning performance improved in the presence of counterfactual feedback in both healthy controls and unmedicated GTS patients, this was not the case in aripiprazole-medicated GTS patients. Our results suggest that whereas aripiprazole preserves direct learning of action-outcome associations, it may impair more complex inferential processes, such as counterfactual learning from forgone outcomes, in GTS patients treated with this medication.

Liquid computing on and off the edge of chaos with a striatal microcircuit

PubMed Central

Toledo-Suárez, Carlos; Duarte, Renato; Morrison, Abigail

2014-01-01

In reinforcement learning theories of the basal ganglia, there is a need for the expected rewards corresponding to relevant environmental states to be maintained and modified during the learning process. However, the representation of these states that allows them to be associated with reward expectations remains unclear. Previous studies have tended to rely on pre-defined partitioning of states encoded by disjunct neuronal groups or sparse topological drives. A more likely scenario is that striatal neurons are involved in the encoding of multiple different states through their spike patterns, and that an appropriate partitioning of an environment is learned on the basis of task constraints, thus minimizing the number of states involved in solving a particular task. Here we show that striatal activity is sufficient to implement a liquid state, an important prerequisite for such a computation, whereby transient patterns of striatal activity are mapped onto the relevant states. We develop a simple small scale model of the striatum which can reproduce key features of the experimentally observed activity of the major cell types of the striatum. We then use the activity of this network as input for the supervised training of four simple linear readouts to learn three different functions on a plane, where the network is stimulated with the spike coded position of the agent. We discover that the network configuration that best reproduces striatal activity statistics lies on the edge of chaos and has good performance on all three tasks, but that in general, the edge of chaos is a poor predictor of network performance. PMID:25484864

Somatosensory Contribution to the Initial Stages of Human Motor Learning

PubMed Central

Bernardi, Nicolò F.; Darainy, Mohammad

2015-01-01

The early stages of motor skill acquisition are often marked by uncertainty about the sensory and motor goals of the task, as is the case in learning to speak or learning the feel of a good tennis serve. Here we present an experimental model of this early learning process, in which targets are acquired by exploration and reinforcement rather than sensory error. We use this model to investigate the relative contribution of motor and sensory factors to human motor learning. Participants make active reaching movements or matched passive movements to an unseen target using a robot arm. We find that learning through passive movements paired with reinforcement is comparable with learning associated with active movement, both in terms of magnitude and durability, with improvements due to training still observable at a 1 week retest. Motor learning is also accompanied by changes in somatosensory perceptual acuity. No stable changes in motor performance are observed for participants that train, actively or passively, in the absence of reinforcement, or for participants who are given explicit information about target position in the absence of somatosensory experience. These findings indicate that the somatosensory system dominates learning in the early stages of motor skill acquisition. SIGNIFICANCE STATEMENT The research focuses on the initial stages of human motor learning, introducing a new experimental model that closely approximates the key features of motor learning outside of the laboratory. The finding indicates that it is the somatosensory system rather than the motor system that dominates learning in the early stages of motor skill acquisition. This is important given that most of our computational models of motor learning are based on the idea that learning is motoric in origin. This is also a valuable finding for rehabilitation of patients with limited mobility as it shows that reinforcement in conjunction with passive movement results in benefits to motor learning that are as great as those observed for active movement training. PMID:26490869

Somatic and Reinforcement-Based Plasticity in the Initial Stages of Human Motor Learning.

PubMed

Sidarta, Ananda; Vahdat, Shahabeddin; Bernardi, Nicolò F; Ostry, David J

2016-11-16

As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning. The neuroimaging data revealed reinforcement-related changes in both motor and somatosensory brain areas in which a strengthening of connectivity was related to the amount of positive reinforcement during learning. Areas of prefrontal cortex were similarly altered in relation to reinforcement, with connectivity between sensorimotor areas of putamen and the reward-related ventromedial prefrontal cortex strengthened in relation to the amount of successful feedback received. In other analyses, we assessed connectivity related to changes in movement direction between trials, a type of variability that presumably reflects exploratory strategies during learning. We found that connectivity in a network linking motor and somatosensory cortices increased with trial-to-trial changes in direction. Connectivity varied as well with the change in movement direction following incorrect movements. Here the changes were observed in a somatic memory and decision making network involving ventrolateral prefrontal cortex and second somatosensory cortex. Our results point to the idea that the initial stages of motor learning are not wholly motor but rather involve plasticity in somatic and prefrontal networks related both to reward and exploration. In the initial stages of motor learning, the placement of the limbs is learned primarily through trial and error. In an experimental analog, participants make reaching movements to a hidden target and receive positive feedback when successful. We identified sources of plasticity based on changes in functional connectivity using resting-state fMRI. The main finding is that there is a strengthening of connectivity between reward-related prefrontal areas and sensorimotor areas in the basal ganglia and frontal cortex. There is also a strengthening of connectivity related to movement exploration in sensorimotor circuits involved in somatic memory and decision making. The results indicate that initial stages of motor learning depend on plasticity in somatic and prefrontal networks related to reward and exploration. Copyright © 2016 the authors 0270-6474/16/3611682-11$15.00/0.

Somatic and Reinforcement-Based Plasticity in the Initial Stages of Human Motor Learning

PubMed Central

Sidarta, Ananda; Vahdat, Shahabeddin; Bernardi, Nicolò F.

2016-01-01

As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning. The neuroimaging data revealed reinforcement-related changes in both motor and somatosensory brain areas in which a strengthening of connectivity was related to the amount of positive reinforcement during learning. Areas of prefrontal cortex were similarly altered in relation to reinforcement, with connectivity between sensorimotor areas of putamen and the reward-related ventromedial prefrontal cortex strengthened in relation to the amount of successful feedback received. In other analyses, we assessed connectivity related to changes in movement direction between trials, a type of variability that presumably reflects exploratory strategies during learning. We found that connectivity in a network linking motor and somatosensory cortices increased with trial-to-trial changes in direction. Connectivity varied as well with the change in movement direction following incorrect movements. Here the changes were observed in a somatic memory and decision making network involving ventrolateral prefrontal cortex and second somatosensory cortex. Our results point to the idea that the initial stages of motor learning are not wholly motor but rather involve plasticity in somatic and prefrontal networks related both to reward and exploration. SIGNIFICANCE STATEMENT In the initial stages of motor learning, the placement of the limbs is learned primarily through trial and error. In an experimental analog, participants make reaching movements to a hidden target and receive positive feedback when successful. We identified sources of plasticity based on changes in functional connectivity using resting-state fMRI. The main finding is that there is a strengthening of connectivity between reward-related prefrontal areas and sensorimotor areas in the basal ganglia and frontal cortex. There is also a strengthening of connectivity related to movement exploration in sensorimotor circuits involved in somatic memory and decision making. The results indicate that initial stages of motor learning depend on plasticity in somatic and prefrontal networks related to reward and exploration. PMID:27852776

[Anaesthetists learn--do institutions also learn? Importance of institutional learning and corporate culture in clinics].

PubMed

Schüpfer, G; Gfrörer, R; Schleppers, A

2007-10-01

In only a few contexts is the need for substantial learning more pronounced than in health care. For a health care provider, the ability to learn is essential in a changing environment. Although individual humans are programmed to learn naturally, organisations are not. Learning that is limited to individual professions and traditional approaches to continuing medical education is not sufficient to bring about substantial changes in the learning capacity of an institution. Also, organisational learning is an important issue for anaesthesia departments. Future success of an organisation often depends on new capabilities and competencies. Organisational learning is the capacity or processes within an organisation to maintain or improve performance based on experience. Learning is seen as a system-level phenomenon as it stays in the organisation regardless of the players involved. Experience from other industries shows that learning strategies tend to focus on single loop learning, with relatively little double loop learning and virtually no meta-learning or non-learning. The emphasis on team delivery of health care reinforces the need for team learning. Learning organisations make learning an intrinsic part of their organisations and are a place where people continually learn how to learn together. Organisational learning practice can help to improve existing skills and competencies and to change outdated assumptions, procedures and structures. So far, learning theory has been ignored in medicine, due to a wide variety of complex political, economic, social, organisational culture and medical factors that prevent innovation and resist change. The organisational culture is central to every stage of the learning process. Learning organisations move beyond simple employee training into organisational problem solving, innovation and learning. Therefore, teamwork and leadership are necessary. Successful organisations change the competencies of individuals, the systems, the organisation, the strategy and the culture.

"The stone which the builders rejected...": Delay of reinforcement and response rate on fixed-interval and related schedules.

PubMed

Wearden, J H; Lejeune, Helga

2006-02-28

The article deals with response rates (mainly running and peak or terminal rates) on simple and on some mixed-FI schedules and explores the idea that these rates are determined by the average delay of reinforcement for responses occurring during the response periods that the schedules generate. The effects of reinforcement delay are assumed to be mediated by a hyperbolic delay of reinforcement gradient. The account predicts that (a) running rates on simple FI schedules should increase with increasing rate of reinforcement, in a manner close to that required by Herrnstein's equation, (b) improving temporal control during acquisition should be associated with increasing running rates, (c) two-valued mixed-FI schedules with equiprobable components should produce complex results, with peak rates sometimes being higher on the longer component schedule, and (d) that effects of reinforcement probability on mixed-FI should affect the response rate at the time of the shorter component only. All these predictions were confirmed by data, although effects in some experiments remain outside the scope of the model. In general, delay of reinforcement as a determinant of response rate on FI and related schedules (rather than temporal control on such schedules) seems a useful starting point for a more thorough analysis of some neglected questions about performance on FI and related schedules.

What is the optimal task difficulty for reinforcement learning of brain self-regulation?

PubMed

Bauer, Robert; Vukelić, Mathias; Gharabaghi, Alireza

2016-09-01

The balance between action and reward during neurofeedback may influence reinforcement learning of brain self-regulation. Eleven healthy volunteers participated in three runs of motor imagery-based brain-machine interface feedback where a robot passively opened the hand contingent to β-band modulation. For each run, the β-desynchronization threshold to initiate the hand robot movement increased in difficulty (low, moderate, and demanding). In this context, the incentive to learn was estimated by the change of reward per action, operationalized as the change in reward duration per movement onset. Variance analysis revealed a significant interaction between threshold difficulty and the relationship between reward duration and number of movement onsets (p<0.001), indicating a negative learning incentive for low difficulty, but a positive learning incentive for moderate and demanding runs. Exploration of different thresholds in the same data set indicated that the learning incentive peaked at higher thresholds than the threshold which resulted in maximum classification accuracy. Specificity is more important than sensitivity of neurofeedback for reinforcement learning of brain self-regulation. Learning efficiency requires adequate challenge by neurofeedback interventions. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The Computational Development of Reinforcement Learning during Adolescence

PubMed Central

Palminteri, Stefano; Coricelli, Giorgio; Blakemore, Sarah-Jayne

2016-01-01

Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents’ behaviour was better explained by a basic reinforcement learning algorithm, adults’ behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence. PMID:27322574

Robust reinforcement learning.

PubMed

Morimoto, Jun; Doya, Kenji

2005-02-01

This letter proposes a new reinforcement learning (RL) paradigm that explicitly takes into account input disturbance as well as modeling errors. The use of environmental models in RL is quite popular for both offline learning using simulations and for online action planning. However, the difference between the model and the real environment can lead to unpredictable, and often unwanted, results. Based on the theory of H(infinity) control, we consider a differential game in which a "disturbing" agent tries to make the worst possible disturbance while a "control" agent tries to make the best control input. The problem is formulated as finding a min-max solution of a value function that takes into account the amount of the reward and the norm of the disturbance. We derive online learning algorithms for estimating the value function and for calculating the worst disturbance and the best control in reference to the value function. We tested the paradigm, which we call robust reinforcement learning (RRL), on the control task of an inverted pendulum. In the linear domain, the policy and the value function learned by online algorithms coincided with those derived analytically by the linear H(infinity) control theory. For a fully nonlinear swing-up task, RRL achieved robust performance with changes in the pendulum weight and friction, while a standard reinforcement learning algorithm could not deal with these changes. We also applied RRL to the cart-pole swing-up task, and a robust swing-up policy was acquired.

Coexistence of Reward and Unsupervised Learning During the Operant Conditioning of Neural Firing Rates

PubMed Central

Kerr, Robert R.; Grayden, David B.; Thomas, Doreen A.; Gilson, Matthieu; Burkitt, Anthony N.

2014-01-01

A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP), and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards). We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD). We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments. PMID:24475240

Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions

PubMed Central

Fee, Michale S.

2012-01-01

In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current “time” in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources. PMID:22754501

Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions.

PubMed

Fee, Michale S

2012-01-01

In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current "time" in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources.

Histidine-decarboxylase knockout mice show deficient nonreinforced episodic object memory, improved negatively reinforced water-maze performance, and increased neo- and ventro-striatal dopamine turnover.

PubMed

Dere, Ekrem; De Souza-Silva, Maria A; Topic, Bianca; Spieler, Richard E; Haas, Helmut L; Huston, Joseph P

2003-01-01

The brain's histaminergic system has been implicated in hippocampal synaptic plasticity, learning, and memory, as well as brain reward and reinforcement. Our past pharmacological and lesion studies indicated that the brain's histamine system exerts inhibitory effects on the brain's reinforcement respective reward system reciprocal to mesolimbic dopamine systems, thereby modulating learning and memory performance. Given the close functional relationship between brain reinforcement and memory processes, the total disruption of brain histamine synthesis via genetic disruption of its synthesizing enzyme, histidine decarboxylase (HDC), in the mouse might have differential effects on learning dependent on the task-inherent reinforcement contingencies. Here, we investigated the effects of an HDC gene disruption in the mouse in a nonreinforced object exploration task and a negatively reinforced water-maze task as well as on neo- and ventro-striatal dopamine systems known to be involved in brain reward and reinforcement. Histidine decarboxylase knockout (HDC-KO) mice had higher dihydrophenylacetic acid concentrations and a higher dihydrophenylacetic acid/dopamine ratio in the neostriatum. In the ventral striatum, dihydrophenylacetic acid/dopamine and 3-methoxytyramine/dopamine ratios were higher in HDC-KO mice. Furthermore, the HDC-KO mice showed improved water-maze performance during both hidden and cued platform tasks, but deficient object discrimination based on temporal relationships. Our data imply that disruption of brain histamine synthesis can have both memory promoting and suppressive effects via distinct and independent mechanisms and further indicate that these opposed effects are related to the task-inherent reinforcement contingencies.

Dorsal Striatal-Midbrain Connectivity in Humans Predicts How Reinforcements Are Used to Guide Decisions

ERIC Educational Resources Information Center

Kahnt, Thorsten; Park, Soyoung Q.; Cohen, Michael X.; Beck, Anne; Heinz, Andreas; Wrase, Jana

2009-01-01

It has been suggested that the target areas of dopaminergic midbrain neurons, the dorsal (DS) and ventral striatum (VS), are differently involved in reinforcement learning especially as actor and critic. Whereas the critic learns to predict rewards, the actor maintains action values to guide future decisions. The different midbrain connections to…

Autonomous Inter-Task Transfer in Reinforcement Learning Domains

DTIC Science & Technology

2008-08-01

Twentieth International Joint Conference on Artificial Intelli - gence, 2007. 304 Fumihide Tanaka and Masayuki Yamamura. Multitask reinforcement learning...Functions . . . . . . . . . . . . . . . . . . . . . . 17 2.2.3 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Instance-based...tures [Laird et al., 1986, Choi et al., 2007]. However, TL for RL tasks has only recently been gaining attention in the artificial intelligence

A look at Behaviourism and Perceptual Control Theory in Interface Design

DTIC Science & Technology

1998-02-01

behaviours such as response variability, instinctive drift, autoshaping , etc. Perceptual Control Theory (PCT) postulates that behaviours result from the...internal variables. Behaviourism, on the other hand, can not account for variability in responses, instinctive drift, autoshaping , etc. Researchers... Autoshaping . Animals appear to learn without reinforcement. However, conditioning theory speculates that learning results only when reinforcement

BEHAVIORAL MECHANISMS UNDERLYING NICOTINE REINFORCEMENT

PubMed Central

Rupprecht, Laura E.; Smith, Tracy T.; Schassburger, Rachel L.; Buffalari, Deanne M.; Sved, Alan F.; Donny, Eric C.

2015-01-01

Cigarette smoking is the leading cause of preventable deaths worldwide and nicotine, the primary psychoactive constituent in tobacco, drives sustained use. The behavioral actions of nicotine are complex and extend well beyond the actions of the drug as a primary reinforcer. Stimuli that are consistently paired with nicotine can, through associative learning, take on reinforcing properties as conditioned stimuli. These conditioned stimuli can then impact the rate and probability of behavior and even function as conditioning reinforcers that maintain behavior in the absence of nicotine. Nicotine can also act as a conditioned stimulus, predicting the delivery of other reinforcers, which may allow nicotine to acquire value as a conditioned reinforcer. These associative effects, establishing non-nicotine stimuli as conditioned stimuli with discriminative stimulus and conditioned reinforcing properties as well as establishing nicotine as a conditioned stimulus, are predicted by basic conditioning principles. However, nicotine can also act non-associatively. Nicotine directly enhances the reinforcing efficacy of other reinforcing stimuli in the environment, an effect that does not require a temporal or predictive relationship between nicotine and either the stimulus or the behavior. Hence, the reinforcing actions of nicotine stem both from the primary reinforcing actions of the drug (and the subsequent associative learning effects) as well as the reinforcement enhancement action of nicotine which is non-associative in nature. Gaining a better understanding of how nicotine impacts behavior will allow for maximally effective tobacco control efforts aimed at reducing the harm associated with tobacco use by reducing and/or treating its addictiveness. PMID:25638333

Measuring reinforcement learning and motivation constructs in experimental animals: relevance to the negative symptoms of schizophrenia.

PubMed

Markou, Athina; Salamone, John D; Bussey, Timothy J; Mar, Adam C; Brunner, Daniela; Gilmour, Gary; Balsam, Peter

2013-11-01

The present review article summarizes and expands upon the discussions that were initiated during a meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS; http://cntrics.ucdavis.edu) meeting. A major goal of the CNTRICS meeting was to identify experimental procedures and measures that can be used in laboratory animals to assess psychological constructs that are related to the psychopathology of schizophrenia. The issues discussed in this review reflect the deliberations of the Motivation Working Group of the CNTRICS meeting, which included most of the authors of this article as well as additional participants. After receiving task nominations from the general research community, this working group was asked to identify experimental procedures in laboratory animals that can assess aspects of reinforcement learning and motivation that may be relevant for research on the negative symptoms of schizophrenia, as well as other disorders characterized by deficits in reinforcement learning and motivation. The tasks described here that assess reinforcement learning are the Autoshaping Task, Probabilistic Reward Learning Tasks, and the Response Bias Probabilistic Reward Task. The tasks described here that assess motivation are Outcome Devaluation and Contingency Degradation Tasks and Effort-Based Tasks. In addition to describing such methods and procedures, the present article provides a working vocabulary for research and theory in this field, as well as an industry perspective about how such tasks may be used in drug discovery. It is hoped that this review can aid investigators who are conducting research in this complex area, promote translational studies by highlighting shared research goals and fostering a common vocabulary across basic and clinical fields, and facilitate the development of medications for the treatment of symptoms mediated by reinforcement learning and motivational deficits. Copyright © 2013 Elsevier Ltd. All rights reserved.

Goal-Directed and Habit-Like Modulations of Stimulus Processing during Reinforcement Learning.

PubMed

Luque, David; Beesley, Tom; Morris, Richard W; Jack, Bradley N; Griffiths, Oren; Whitford, Thomas J; Le Pelley, Mike E

2017-03-15

Recent research has shown that perceptual processing of stimuli previously associated with high-value rewards is automatically prioritized even when rewards are no longer available. It has been hypothesized that such reward-related modulation of stimulus salience is conceptually similar to an "attentional habit." Recording event-related potentials in humans during a reinforcement learning task, we show strong evidence in favor of this hypothesis. Resistance to outcome devaluation (the defining feature of a habit) was shown by the stimulus-locked P1 component, reflecting activity in the extrastriate visual cortex. Analysis at longer latencies revealed a positive component (corresponding to the P3b, from 550-700 ms) sensitive to outcome devaluation. Therefore, distinct spatiotemporal patterns of brain activity were observed corresponding to habitual and goal-directed processes. These results demonstrate that reinforcement learning engages both attentional habits and goal-directed processes in parallel. Consequences for brain and computational models of reinforcement learning are discussed. SIGNIFICANCE STATEMENT The human attentional network adapts to detect stimuli that predict important rewards. A recent hypothesis suggests that the visual cortex automatically prioritizes reward-related stimuli, driven by cached representations of reward value; that is, stimulus-response habits. Alternatively, the neural system may track the current value of the predicted outcome. Our results demonstrate for the first time that visual cortex activity is increased for reward-related stimuli even when the rewarding event is temporarily devalued. In contrast, longer-latency brain activity was specifically sensitive to transient changes in reward value. Therefore, we show that both habit-like attention and goal-directed processes occur in the same learning episode at different latencies. This result has important consequences for computational models of reinforcement learning. Copyright © 2017 the authors 0270-6474/17/373009-09$15.00/0.

Measuring reinforcement learning and motivation constructs in experimental animals: relevance to the negative symptoms of schizophrenia

PubMed Central

Markou, Athina; Salamone, John D.; Bussey, Timothy; Mar, Adam; Brunner, Daniela; Gilmour, Gary; Balsam, Peter

2013-01-01

The present review article summarizes and expands upon the discussions that were initiated during a meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS; http://cntrics.ucdavis.edu). A major goal of the CNTRICS meeting was to identify experimental procedures and measures that can be used in laboratory animals to assess psychological constructs that are related to the psychopathology of schizophrenia. The issues discussed in this review reflect the deliberations of the Motivation Working Group of the CNTRICS meeting, which included most of the authors of this article as well as additional participants. After receiving task nominations from the general research community, this working group was asked to identify experimental procedures in laboratory animals that can assess aspects of reinforcement learning and motivation that may be relevant for research on the negative symptoms of schizophrenia, as well as other disorders characterized by deficits in reinforcement learning and motivation. The tasks described here that assess reinforcement learning are the Autoshaping Task, Probabilistic Reward Learning Tasks, and the Response Bias Probabilistic Reward Task. The tasks described here that assess motivation are Outcome Devaluation and Contingency Degradation Tasks and Effort-Based Tasks. In addition to describing such methods and procedures, the present article provides a working vocabulary for research and theory in this field, as well as an industry perspective about how such tasks may be used in drug discovery. It is hoped that this review can aid investigators who are conducting research in this complex area, promote translational studies by highlighting shared research goals and fostering a common vocabulary across basic and clinical fields, and facilitate the development of medications for the treatment of symptoms mediated by reinforcement learning and motivational deficits. PMID:23994273

Feature Reinforcement Learning: Part I. Unstructured MDPs

NASA Astrophysics Data System (ADS)

Hutter, Marcus

2009-12-01

General-purpose, intelligent, learning agents cycle through sequences of observations, actions, and rewards that are complex, uncertain, unknown, and non-Markovian. On the other hand, reinforcement learning is well-developed for small finite state Markov decision processes (MDPs). Up to now, extracting the right state representations out of bare observations, that is, reducing the general agent setup to the MDP framework, is an art that involves significant effort by designers. The primary goal of this work is to automate the reduction process and thereby significantly expand the scope of many existing reinforcement learning algorithms and the agents that employ them. Before we can think of mechanizing this search for suitable MDPs, we need a formal objective criterion. The main contribution of this article is to develop such a criterion. I also integrate the various parts into one learning algorithm. Extensions to more realistic dynamic Bayesian networks are developed in Part II (Hutter, 2009c). The role of POMDPs is also considered there.

The role of within-compound associations in learning about absent cues.

PubMed

Witnauer, James E; Miller, Ralph R

2011-05-01

When two cues are reinforced together (in compound), most associative models assume that animals learn an associative network that includes direct cue-outcome associations and a within-compound association. All models of associative learning subscribe to the importance of cue-outcome associations, but most models assume that within-compound associations are irrelevant to each cue's subsequent behavioral control. In the present article, we present an extension of Van Hamme and Wasserman's (Learning and Motivation 25:127-151, 1994) model of retrospective revaluation based on learning about absent cues that are retrieved through within-compound associations. The model was compared with a model lacking retrieval through within-compound associations. Simulations showed that within-compound associations are necessary for the model to explain higher-order retrospective revaluation and the observed greater retrospective revaluation after partial reinforcement than after continuous reinforcement alone. These simulations suggest that the associability of an absent stimulus is determined by the extent to which the stimulus is activated through the within-compound association.

The role of within-compound associations in learning about absent cues

PubMed Central

Witnauer, James E.

2011-01-01

When two cues are reinforced together (in compound), most associative models assume that animals learn an associative network that includes direct cue–outcome associations and a within-compound association. All models of associative learning subscribe to the importance of cue–outcome associations, but most models assume that within-compound associations are irrelevant to each cue's subsequent behavioral control. In the present article, we present an extension of Van Hamme and Wasserman's (Learning and Motivation 25:127–151, 1994) model of retrospective revaluation based on learning about absent cues that are retrieved through within-compound associations. The model was compared with a model lacking retrieval through within-compound associations. Simulations showed that within-compound associations are necessary for the model to explain higher-order retrospective revaluation and the observed greater retrospective revaluation after partial reinforcement than after continuous reinforcement alone. These simulations suggest that the associability of an absent stimulus is determined by the extent to which the stimulus is activated through the within-compound association. PMID:21264569

Pleasurable music affects reinforcement learning according to the listener

PubMed Central

Gold, Benjamin P.; Frank, Michael J.; Bogert, Brigitte; Brattico, Elvira

2013-01-01

Mounting evidence links the enjoyment of music to brain areas implicated in emotion and the dopaminergic reward system. In particular, dopamine release in the ventral striatum seems to play a major role in the rewarding aspect of music listening. Striatal dopamine also influences reinforcement learning, such that subjects with greater dopamine efficacy learn better to approach rewards while those with lesser dopamine efficacy learn better to avoid punishments. In this study, we explored the practical implications of musical pleasure through its ability to facilitate reinforcement learning via non-pharmacological dopamine elicitation. Subjects from a wide variety of musical backgrounds chose a pleasurable and a neutral piece of music from an experimenter-compiled database, and then listened to one or both of these pieces (according to pseudo-random group assignment) as they performed a reinforcement learning task dependent on dopamine transmission. We assessed musical backgrounds as well as typical listening patterns with the new Helsinki Inventory of Music and Affective Behaviors (HIMAB), and separately investigated behavior for the training and test phases of the learning task. Subjects with more musical experience trained better with neutral music and tested better with pleasurable music, while those with less musical experience exhibited the opposite effect. HIMAB results regarding listening behaviors and subjective music ratings indicate that these effects arose from different listening styles: namely, more affective listening in non-musicians and more analytical listening in musicians. In conclusion, musical pleasure was able to influence task performance, and the shape of this effect depended on group and individual factors. These findings have implications in affective neuroscience, neuroaesthetics, learning, and music therapy. PMID:23970875

Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity.

PubMed

Blake, David T

2017-06-18

The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience-dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms. Changes occur with greatest ease in neurons containing α-CamKII, which are pyramidal neurons in layers II/III and layers V/VI. These changes use synaptic mechanisms including long term depression. Synaptic strengthening at NMDA-containing synapses does occur, but its weak association with activity suggests other factors also initiate changes. Studies that control learning find support of reinforcement learning rules and limited evidence of other forms of supervised learning. Behaviorally associating a stimulus with reinforcement leads to a strengthening of cortical response strength and enlarging of response area with poor selectivity. Associating a stimulus with omission of reinforcement leads to a selective weakening of responses. In some preparations in which these associations are not as clearly made, neurons with the most informative discharges are relatively stronger after training. Studies analyzing the temporal profile of responses associated with omission of reward, or of plasticity in studies with different discriminanda but statistically matched stimuli, support the existence of limited supervised network learning. © 2017 American Physiological Society. Compr Physiol 7:977-1008, 2017. Copyright © 2017 John Wiley & Sons, Inc.

Feedback from the heart: Emotional learning and memory is controlled by cardiac cycle, interoceptive accuracy and personality.

PubMed

Pfeifer, Gaby; Garfinkel, Sarah N; Gould van Praag, Cassandra D; Sahota, Kuljit; Betka, Sophie; Critchley, Hugo D

2017-05-01

Feedback processing is critical to trial-and-error learning. Here, we examined whether interoceptive signals concerning the state of cardiovascular arousal influence the processing of reinforcing feedback during the learning of 'emotional' face-name pairs, with subsequent effects on retrieval. Participants (N=29) engaged in a learning task of face-name pairs (fearful, neutral, happy faces). Correct and incorrect learning decisions were reinforced by auditory feedback, which was delivered either at cardiac systole (on the heartbeat, when baroreceptors signal the contraction of the heart to the brain), or at diastole (between heartbeats during baroreceptor quiescence). We discovered a cardiac influence on feedback processing that enhanced the learning of fearful faces in people with heightened interoceptive ability. Individuals with enhanced accuracy on a heartbeat counting task learned fearful face-name pairs better when feedback was given at systole than at diastole. This effect was not present for neutral and happy faces. At retrieval, we also observed related effects of personality: First, individuals scoring higher for extraversion showed poorer retrieval accuracy. These individuals additionally manifested lower resting heart rate and lower state anxiety, suggesting that attenuated levels of cardiovascular arousal in extraverts underlies poorer performance. Second, higher extraversion scores predicted higher emotional intensity ratings of fearful faces reinforced at systole. Third, individuals scoring higher for neuroticism showed higher retrieval confidence for fearful faces reinforced at diastole. Our results show that cardiac signals shape feedback processing to influence learning of fearful faces, an effect underpinned by personality differences linked to psychophysiological arousal. Copyright © 2017 Elsevier B.V. All rights reserved.

A reinforcement learning-based architecture for fuzzy logic control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1992-01-01

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning.

PubMed

Franklin, Nicholas T; Frank, Michael J

2015-12-25

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments.

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

PubMed

Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

2011-01-01

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning.

PubMed

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

PubMed Central

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

NASA Technical Reports Server (NTRS)

Jani, Yashvant

1992-01-01

The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Evidence for a neural law of effect.

PubMed

Athalye, Vivek R; Santos, Fernando J; Carmena, Jose M; Costa, Rui M

2018-03-02

Thorndike's law of effect states that actions that lead to reinforcements tend to be repeated more often. Accordingly, neural activity patterns leading to reinforcement are also reentered more frequently. Reinforcement relies on dopaminergic activity in the ventral tegmental area (VTA), and animals shape their behavior to receive dopaminergic stimulation. Seeking evidence for a neural law of effect, we found that mice learn to reenter more frequently motor cortical activity patterns that trigger optogenetic VTA self-stimulation. Learning was accompanied by gradual shaping of these patterns, with participating neurons progressively increasing and aligning their covariance to that of the target pattern. Motor cortex patterns that lead to phasic dopaminergic VTA activity are progressively reinforced and shaped, suggesting a mechanism by which animals select and shape actions to reliably achieve reinforcement. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Active-learning strategies: the use of a game to reinforce learning in nursing education. A case study.

PubMed

Boctor, Lisa

2013-03-01

The majority of nursing students are kinesthetic learners, preferring a hands-on, active approach to education. Research shows that active-learning strategies can increase student learning and satisfaction. This study looks at the use of one active-learning strategy, a Jeopardy-style game, 'Nursopardy', to reinforce Fundamentals of Nursing material, aiding in students' preparation for a standardized final exam. The game was created keeping students varied learning styles and the NCLEX blueprint in mind. The blueprint was used to create 5 categories, with 26 total questions. Student survey results, using a five-point Likert scale showed that they did find this learning method enjoyable and beneficial to learning. More research is recommended regarding learning outcomes, when using active-learning strategies, such as games. Copyright © 2012 Elsevier Ltd. All rights reserved.

The "proactive" model of learning: Integrative framework for model-free and model-based reinforcement learning utilizing the associative learning-based proactive brain concept.

PubMed

Zsuga, Judit; Biro, Klara; Papp, Csaba; Tajti, Gabor; Gesztelyi, Rudolf

2016-02-01

Reinforcement learning (RL) is a powerful concept underlying forms of associative learning governed by the use of a scalar reward signal, with learning taking place if expectations are violated. RL may be assessed using model-based and model-free approaches. Model-based reinforcement learning involves the amygdala, the hippocampus, and the orbitofrontal cortex (OFC). The model-free system involves the pedunculopontine-tegmental nucleus (PPTgN), the ventral tegmental area (VTA) and the ventral striatum (VS). Based on the functional connectivity of VS, model-free and model based RL systems center on the VS that by integrating model-free signals (received as reward prediction error) and model-based reward related input computes value. Using the concept of reinforcement learning agent we propose that the VS serves as the value function component of the RL agent. Regarding the model utilized for model-based computations we turned to the proactive brain concept, which offers an ubiquitous function for the default network based on its great functional overlap with contextual associative areas. Hence, by means of the default network the brain continuously organizes its environment into context frames enabling the formulation of analogy-based association that are turned into predictions of what to expect. The OFC integrates reward-related information into context frames upon computing reward expectation by compiling stimulus-reward and context-reward information offered by the amygdala and hippocampus, respectively. Furthermore we suggest that the integration of model-based expectations regarding reward into the value signal is further supported by the efferent of the OFC that reach structures canonical for model-free learning (e.g., the PPTgN, VTA, and VS). (c) 2016 APA, all rights reserved).

Incorporating Dispositional Traits into the Treatment of Anorexia Nervosa

PubMed Central

Herzog, David; Moskovich, Ashley; Merwin, Rhonda; Lin, Tammy

2014-01-01

We provide a general framework to guide the development of interventions that aim to address persistent features in eating disorders that may preclude effective treatment. Using perfectionism as an exemplar, we draw from research in cognitive neuroscience regarding attention and reinforcement learning, from learning theory and social psychology regarding vicarious learning and implications for the role modeling of significant others, and from clinical psychology on the importance of verbal narratives as barriers that may influence expectations and shape reinforcement schedules. PMID:21243482

Hybrid learning in signalling games

NASA Astrophysics Data System (ADS)

Barrett, Jeffrey A.; Cochran, Calvin T.; Huttegger, Simon; Fujiwara, Naoki

2017-09-01

Lewis-Skyrms signalling games have been studied under a variety of low-rationality learning dynamics. Reinforcement dynamics are stable but slow and prone to evolving suboptimal signalling conventions. A low-inertia trial-and-error dynamical like win-stay/lose-randomise is fast and reliable at finding perfect signalling conventions but unstable in the context of noise or agent error. Here we consider a low-rationality hybrid of reinforcement and win-stay/lose-randomise learning that exhibits the virtues of both. This hybrid dynamics is reliable, stable and exceptionally fast.

Discrete Serotonin Systems Mediate Memory Enhancement and Escape Latencies after Unpredicted Aversive Experience in Drosophila Place Memory

PubMed Central

Sitaraman, Divya; Kramer, Elizabeth F.; Kahsai, Lily; Ostrowski, Daniela; Zars, Troy

2017-01-01

Feedback mechanisms in operant learning are critical for animals to increase reward or reduce punishment. However, not all conditions have a behavior that can readily resolve an event. Animals must then try out different behaviors to better their situation through outcome learning. This form of learning allows for novel solutions and with positive experience can lead to unexpected behavioral routines. Learned helplessness, as a type of outcome learning, manifests in part as increases in escape latency in the face of repeated unpredicted shocks. Little is known about the mechanisms of outcome learning. When fruit fly Drosophila melanogaster are exposed to unpredicted high temperatures in a place learning paradigm, flies both increase escape latencies and have a higher memory when given control of a place/temperature contingency. Here we describe discrete serotonin neuronal circuits that mediate aversive reinforcement, escape latencies, and memory levels after place learning in the presence and absence of unexpected aversive events. The results show that two features of learned helplessness depend on the same modulatory system as aversive reinforcement. Moreover, changes in aversive reinforcement and escape latency depend on local neural circuit modulation, while memory enhancement requires larger modulation of multiple behavioral control circuits. PMID:29321732

Tonic or Phasic Stimulation of Dopaminergic Projections to Prefrontal Cortex Causes Mice to Maintain or Deviate from Previously Learned Behavioral Strategies

PubMed Central

Ellwood, Ian T.; Patel, Tosha; Wadia, Varun; Lee, Anthony T.; Liptak, Alayna T.

2017-01-01

Dopamine neurons in the ventral tegmental area (VTA) encode reward prediction errors and can drive reinforcement learning through their projections to striatum, but much less is known about their projections to prefrontal cortex (PFC). Here, we studied these projections and observed phasic VTA–PFC fiber photometry signals after the delivery of rewards. Next, we studied how optogenetic stimulation of these projections affects behavior using conditioned place preference and a task in which mice learn associations between cues and food rewards and then use those associations to make choices. Neither phasic nor tonic stimulation of dopaminergic VTA–PFC projections elicited place preference. Furthermore, substituting phasic VTA–PFC stimulation for food rewards was not sufficient to reinforce new cue–reward associations nor maintain previously learned ones. However, the same patterns of stimulation that failed to reinforce place preference or cue–reward associations were able to modify behavior in other ways. First, continuous tonic stimulation maintained previously learned cue–reward associations even after they ceased being valid. Second, delivering phasic stimulation either continuously or after choices not previously associated with reward induced mice to make choices that deviated from previously learned associations. In summary, despite the fact that dopaminergic VTA–PFC projections exhibit phasic increases in activity that are time locked to the delivery of rewards, phasic activation of these projections does not necessarily reinforce specific actions. Rather, dopaminergic VTA–PFC activity can control whether mice maintain or deviate from previously learned cue–reward associations. SIGNIFICANCE STATEMENT Dopaminergic inputs from ventral tegmental area (VTA) to striatum encode reward prediction errors and reinforce specific actions; however, it is currently unknown whether dopaminergic inputs to prefrontal cortex (PFC) play similar or distinct roles. Here, we used bulk Ca2+ imaging to show that unexpected rewards or reward-predicting cues elicit phasic increases in the activity of dopaminergic VTA–PFC fibers. However, in multiple behavioral paradigms, we failed to observe reinforcing effects after stimulation of these fibers. In these same experiments, we did find that tonic or phasic patterns of stimulation caused mice to maintain or deviate from previously learned cue–reward associations, respectively. Therefore, although they may exhibit similar patterns of activity, dopaminergic inputs to striatum and PFC can elicit divergent behavioral effects. PMID:28739583

The Effect of a Token Reinforcement Program on the Reading Comprehension of a Learning Disabled Student.

ERIC Educational Resources Information Center

Galbreath, Joy; Feldman, David

The relationship of reading comprehension accuracy and a contingently administered token reinforcement program used with an elementary level learning disabled student in the classroom was examined. The S earned points for each correct answer made after oral reading sessions. At the conclusion of the class he could exchange his points for rewards.…

Cocaine addiction as a homeostatic reinforcement learning disorder.

PubMed

Keramati, Mehdi; Durand, Audrey; Girardeau, Paul; Gutkin, Boris; Ahmed, Serge H

2017-03-01

Drug addiction implicates both reward learning and homeostatic regulation mechanisms of the brain. This has stimulated 2 partially successful theoretical perspectives on addiction. Many important aspects of addiction, however, remain to be explained within a single, unified framework that integrates the 2 mechanisms. Building upon a recently developed homeostatic reinforcement learning theory, the authors focus on a key transition stage of addiction that is well modeled in animals, escalation of drug use, and propose a computational theory of cocaine addiction where cocaine reinforces behavior due to its rapid homeostatic corrective effect, whereas its chronic use induces slow and long-lasting changes in homeostatic setpoint. Simulations show that our new theory accounts for key behavioral and neurobiological features of addiction, most notably, escalation of cocaine use, drug-primed craving and relapse, individual differences underlying dose-response curves, and dopamine D2-receptor downregulation in addicts. The theory also generates unique predictions about cocaine self-administration behavior in rats that are confirmed by new experimental results. Viewing addiction as a homeostatic reinforcement learning disorder coherently explains many behavioral and neurobiological aspects of the transition to cocaine addiction, and suggests a new perspective toward understanding addiction. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Challenges in the Verification of Reinforcement Learning Algorithms

NASA Technical Reports Server (NTRS)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

Refining fuzzy logic controllers with machine learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1994-01-01

In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

PubMed

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

PubMed Central

Zhang, Hongjun; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score. PMID:28894463

Investigation of a Reinforcement-Based Toilet Training Procedure for Children with Autism.

ERIC Educational Resources Information Center

Cicero, Frank R.; Pfadt, Al

2002-01-01

This study evaluated the effectiveness of a reinforcement-based toilet training intervention with three children with autism. Procedures included positive reinforcement, graduated guidance, scheduled practice trials, and forward prompting. All three children reduced urination accidents to zero and learned to request bathroom use spontaneously…

Sex Differences in Reinforcement and Punishment on Prime-Time Television.

ERIC Educational Resources Information Center

Downs, A. Chris; Gowan, Darryl C.

1980-01-01

Television programs were analyzed for frequencies of positive reinforcement and punishment exchanged among performers varying in age and sex. Females were found to more often exhibit and receive reinforcement, whereas males more often exhibited and received punishment. These findings have implications for children's learning of positive and…

Separation of Time-Based and Trial-Based Accounts of the Partial Reinforcement Extinction Effect

PubMed Central

Bouton, Mark E.; Woods, Amanda M.; Todd, Travis P.

2013-01-01

Two appetitive conditioning experiments with rats examined time-based and trial-based accounts of the partial reinforcement extinction effect (PREE). In the PREE, the loss of responding that occurs in extinction is slower when the conditioned stimulus (CS) has been paired with a reinforcer on some of its presentations (partially reinforced) instead of every presentation (continuously reinforced). According to a time-based or “time-accumulation” view (e.g., Gallistel & Gibbon, 2000), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger amount of time has accumulated in the CS over trials. In contrast, according to a trial-based view (e.g., Capaldi, 1967), the PREE occurs because the organism has learned in partial reinforcement to expect the reinforcer after a larger number of CS presentations. Experiment 1 used a procedure that equated partially- and continuously-reinforced groups on their expected times to reinforcement during conditioning. A PREE was still observed. Experiment 2 then used an extinction procedure that allowed time in the CS and the number of trials to accumulate differentially through extinction. The PREE was still evident when responding was examined as a function of expected time units to the reinforcer, but was eliminated when responding was examined as a function of expected trial units to the reinforcer. There was no evidence that the animal responded according to the ratio of time accumulated during the CS in extinction over the time in the CS expected before the reinforcer. The results thus favor a trial-based account over a time-based account of extinction and the PREE. PMID:23962669

Novel reinforcement learning paradigm based on response patterning under interval schedules of reinforcement.

PubMed

Schifani, Christin; Sukhanov, Ilya; Dorofeikova, Mariia; Bespalov, Anton

2017-07-28

There is a need to develop cognitive tasks that address valid neuropsychological constructs implicated in disease mechanisms and can be used in animals and humans to guide novel drug discovery. Present experiments aimed to characterize a novel reinforcement learning task based on a classical operant behavioral phenomenon observed in multiple species - differences in response patterning under variable (VI) vs fixed interval (FI) schedules of reinforcement. Wistar rats were trained to press a lever for food under VI30s and later weekly test sessions were introduced with reinforcement schedule switched to FI30s. During the FI30s test session, post-reinforcement pauses (PRPs) gradually grew towards the end of the session reaching 22-43% of the initial values. Animals could be retrained under VI30s conditions, and FI30s test sessions were repeated over a period of several months without appreciable signs of a practice effect. Administration of the non-competitive N-methyl-d-aspartate (NMDA) receptor antagonist MK-801 ((5S,10R)-(+)-5-Methyl-10,11-dihydro-5H-dibenzo[a,d]cyclohepten-5,10-imine maleate) prior to FI30s sessions prevented adjustment of PRPs associated with the change from VI to FI schedule. This effect was most pronounced at the highest tested dose of MK-801 and appeared to be independent of the effects of this dose on response rates. These results provide initial evidence for the possibility to use different response patterning under VI and FI schedules with equivalent reinforcement density for studying effects of drug treatment on reinforcement learning. Copyright © 2017 Elsevier B.V. All rights reserved.

Distinct roles of three frontal cortical areas in reward-guided behavior

PubMed Central

Noonan, M.P.; Mars, R.B.; Rushworth, M.F.S

2011-01-01

Functional magnetic resonance imaging (fMRI) was used to measure activity in three frontal cortical areas, lateral orbitofrontal cortex (lOFC), medial orbitofrontal cortex/ventromedial frontal cortex (mOFC/vmPFC), and anterior cingulate cortex (ACC) when expectations about type of reward, and not just reward presence or absence, could be learned. Two groups of human subjects learned twelve stimulus-response pairings. In one group (Consistent), correct performances of a given pairing were always reinforced with a specific reward outcome whereas in the other group (Inconsistent), correct performances were reinforced with randomly selected rewards. MOFC/vmPFC and lOFC were not distinguished by simple differences in relative preference for positive and negative outcomes. Instead lOFC activity reflected updating of reward-related associations specific to reward type; lOFC was active whenever informative outcomes allowed updating of reward-related associations regardless of whether the outcomes were positive or negative and the effects were greater when consistent stimulus-outcome and response-outcome mappings were present. A psycho-physiological interaction (PPI) analysis demonstrated changed coupling between lOFC and brain areas for visual object representation, such as perirhinal cortex, and reward-guided learning, such as amygdala, ventral striatum, and habenula /mediodorsal thalamus. By contrast mOFC/vmPFC activity reflected expected values of outcomes and occurrence of positive outcomes, irrespective of consistency of outcome mappings. The third frontal cortical region, ACC, reflected the use of reward type information to guide response selection. ACC activity reflected the probability of selecting the correct response, was greater when consistent outcome mappings were present, and was related to individual differences in propensity to select the correct response. PMID:21976525

Brain Research: Implications for Learning.

ERIC Educational Resources Information Center

Soares, Louise M.; Soares, Anthony T.

Brain research has illuminated several areas of the learning process: (1) learning as association; (2) learning as reinforcement; (3) learning as perception; (4) learning as imitation; (5) learning as organization; (6) learning as individual style; and (7) learning as brain activity. The classic conditioning model developed by Pavlov advanced…

Deep imitation learning for 3D navigation tasks.

PubMed

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

PubMed Central

Hassani, S. A.; Oemisch, M.; Balcarras, M.; Westendorff, S.; Ardid, S.; van der Meer, M. A.; Tiesinga, P.; Womelsdorf, T.

2017-01-01

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework. PMID:28091572

Electrophysiological correlates of observational learning in children.

PubMed

Rodriguez Buritica, Julia M; Eppinger, Ben; Schuck, Nicolas W; Heekeren, Hauke R; Li, Shu-Chen

2016-09-01

Observational learning is an important mechanism for cognitive and social development. However, the neurophysiological mechanisms underlying observational learning in children are not well understood. In this study, we used a probabilistic reward-based observational learning paradigm to compare behavioral and electrophysiological markers of individual and observational reinforcement learning in 8- to 10-year-old children. Specifically, we manipulated the amount of observable information as well as children's similarity in age to the observed person (same-aged child vs. adult) to examine the effects of similarity in age on the integration of observed information in children. We show that the feedback-related negativity (FRN) during individual reinforcement learning reflects the valence of outcomes of own actions. Furthermore, we found that the feedback-related negativity during observational reinforcement learning (oFRN) showed a similar distinction between outcome valences of observed actions. This suggests that the oFRN can serve as a measure of observational learning in middle childhood. Moreover, during observational learning children profited from the additional social information and imitated the choices of their own peers more than those of adults, indicating that children have a tendency to conform more with similar others (e.g. their own peers) compared to dissimilar others (adults). Taken together, our results show that children can benefit from integrating observable information and that oFRN may serve as a measure of observational learning in children. © 2015 John Wiley & Sons Ltd.

Mechanisms and time course of vocal learning and consolidation in the adult songbird.

PubMed

Warren, Timothy L; Tumer, Evren C; Charlesworth, Jonathan D; Brainard, Michael S

2011-10-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry.

Mechanisms and time course of vocal learning and consolidation in the adult songbird

PubMed Central

Tumer, Evren C.; Charlesworth, Jonathan D.; Brainard, Michael S.

2011-01-01

In songbirds, the basal ganglia outflow nucleus LMAN is a cortical analog that is required for several forms of song plasticity and learning. Moreover, in adults, inactivating LMAN can reverse the initial expression of learning driven via aversive reinforcement. In the present study, we investigated how LMAN contributes to both reinforcement-driven learning and a self-driven recovery process in adult Bengalese finches. We first drove changes in the fundamental frequency of targeted song syllables and compared the effects of inactivating LMAN with the effects of interfering with N-methyl-d-aspartate (NMDA) receptor-dependent transmission from LMAN to one of its principal targets, the song premotor nucleus RA. Inactivating LMAN and blocking NMDA receptors in RA caused indistinguishable reversions in the expression of learning, indicating that LMAN contributes to learning through NMDA receptor-mediated glutamatergic transmission to RA. We next assessed how LMAN's role evolves over time by maintaining learned changes to song while periodically inactivating LMAN. The expression of learning consolidated to become LMAN independent over multiple days, indicating that this form of consolidation is not completed over one night, as previously suggested, and instead may occur gradually during singing. Subsequent cessation of reinforcement was followed by a gradual self-driven recovery of original song structure, indicating that consolidation does not correspond with the lasting retention of changes to song. Finally, for self-driven recovery, as for reinforcement-driven learning, LMAN was required for the expression of initial, but not later, changes to song. Our results indicate that NMDA receptor-dependent transmission from LMAN to RA plays an essential role in the initial expression of two distinct forms of vocal learning and that this role gradually wanes over a multiday process of consolidation. The results support an emerging view that cortical-basal ganglia circuits can direct the initial expression of learning via top-down influences on primary motor circuitry. PMID:21734110

Behavior Modification Revisited: Practical Application for the Reading Teacher.

ERIC Educational Resources Information Center

Lang, Deborah Odom; Lang, William Steve

An informal study examined the impact of a simple behavior modification procedure on the reading habits of second and fifth grade students. Behavior modification theory was pared down to two rules ("reinforce proper behavior" and "reinforce anything close to proper behavior") so that no additional burdens would be placed on…

Through the Looking Glass: Symmetry in Behavioral Principles?

ERIC Educational Resources Information Center

Marr, M. Jackson

2006-01-01

In this article, the author discusses and presents seven possibilities that describe how symmetry principles are reflected in behavior analysis. First, if there are apparently no functional distinctions to be made between positive and negative reinforcement, then reinforcer effectiveness (by various measures) is invariant under a simple inversion…

The Effects of Short Interval Delay of Reinforcement Upon Human Discrimination Learning. IMRID Papers and Reports Vol. 4 No. 12.

ERIC Educational Resources Information Center

Kral, Paul A.; And Others

Investigates the effect of delay of reinforcement upon human discrimination learning with particular emphasis on the form of the gradient within the first few seconds of delay. In previous studies subjects are usually required to make an instrumental response to a stimulus, this is followed by the delay interval, and finally, the reinforcement…

Learning Theory and the Typewriter Teacher

ERIC Educational Resources Information Center

Wakin, B. Bertha

1974-01-01

Eight basic principles of learning are described and discussed in terms of practical learning strategies for typewriting. Described are goal setting, preassessment, active participation, individual differences, reinforcement, practice, transfer of learning, and evaluation. (SC)

Disrupted Reinforcement Learning and Maladaptive Behavior in Women with a History of Childhood Sexual Abuse: A High-Density Event-Related Potential Study

PubMed Central

Pechtel, Pia; Pizzagalli, Diego A.

2013-01-01

Context Childhood sexual abuse (CSA) has been associated with psychopathology, particularly major depressive disorder (MDD), and high-risk behaviors. Despite grave epidemiological data, the mechanisms underlying these maladaptive outcomes remain poorly understood. Objective We examined whether CSA history, particularly in conjunction with past MDD, is associated with behavioral and neural dysfunction in reinforcement learning, and whether such dysfunction is linked to maladaptive behavior. Design Participants completed a clinical evaluation and a probabilistic reinforcement task while 128-channel event-related potentials were recorded. Setting Academic setting; participants recruited from the community. Participants Fifteen remitted depressed females with CSA history (CSA+rMDD), 16 remitted depressed females without CSA history (rMDD), and 18 healthy females. Main Outcome Measures Participants’ preference for choosing the most rewarded stimulus and avoiding the most punished stimulus was evaluated. The feedback-related negativity (FRN) and error-related negativity (ERN)–hypothesized to reflect activation in the anterior cingulate cortex–were used as electrophysiological indices of reinforcement learning. Results No group differences emerged in the acquisition of reinforcement contingencies. In trials requiring to rely partially or exclusively on previously rewarded information, the CSA+rMDD group showed (1) lower accuracy (relative to both controls and rMDD), (2) blunted electrophysiological differentiation between correct and incorrect responses (relative to controls), and (3) increased activation in the subgenual anterior cingulate cortex (relative to rMDD). CSA history was not associated with impairments in avoiding the most punished stimulus. Self-harm and suicidal behaviors correlated with poorer performance of previously rewarded–but not previously punished–trials. Conclusions Irrespective of past MDD, women with CSA histories showed neural and behavioral deficits in utilizing previous reinforcement to optimize decision-making in the absence of feedback (blunted “Go learning”). While the current study provides initial evidence for reward-specific deficits associated with CSA, future research is warranted to determine if disrupted positive reinforcement learning predicts high-risk behavior following CSA. PMID:23487253

Reinforcing Visual Grouping Cues to Communicate Complex Informational Structure.

PubMed

Bae, Juhee; Watson, Benjamin

2014-12-01

In his book Multimedia Learning [7], Richard Mayer asserts that viewers learn best from imagery that provides them with cues to help them organize new information into the correct knowledge structures. Designers have long been exploiting the Gestalt laws of visual grouping to deliver viewers those cues using visual hierarchy, often communicating structures much more complex than the simple organizations studied in psychological research. Unfortunately, designers are largely practical in their work, and have not paused to build a complex theory of structural communication. If we are to build a tool to help novices create effective and well structured visuals, we need a better understanding of how to create them. Our work takes a first step toward addressing this lack, studying how five of the many grouping cues (proximity, color similarity, common region, connectivity, and alignment) can be effectively combined to communicate structured text and imagery from real world examples. To measure the effectiveness of this structural communication, we applied a digital version of card sorting, a method widely used in anthropology and cognitive science to extract cognitive structures. We then used tree edit distance to measure the difference between perceived and communicated structures. Our most significant findings are: 1) with careful design, complex structure can be communicated clearly; 2) communicating complex structure is best done with multiple reinforcing grouping cues; 3) common region (use of containers such as boxes) is particularly effective at communicating structure; and 4) alignment is a weak structural communicator.

Understanding the contributions of visual stimuli to contextual fear conditioning: A proof-of-concept study using LCD screens.

PubMed

Murawski, Nathen J; Asok, Arun

2017-01-10

The precise contribution of visual information to contextual fear learning and discrimination has remained elusive. To better understand this contribution, we coupled the context pre-exposure facilitation effect (CPFE) fear conditioning paradigm with presentations of distinct visual scenes displayed on 4 LCD screens surrounding a conditioning chamber. Adult male Long-Evans rats received non-reinforced context pre-exposure on Day 1, an immediate 1.5mA foot shock on Day 2, and a non-reinforced context test on Day 3. Rats were pre-exposed to either digital Context (dCtx) A, dCtx B, a distinct Ctx C, or no context on Day 1. Digital context A and B were identical except for the visual image displayed on the LCD screens. Immediate shock and retention testing occurred in dCtx A. Rats pre-exposed dCtx A showed the CPFE with significantly higher levels of freezing compared to controls. Rats pre-exposed to Context B failed to show the CPFE, with freezing that did not highly differ from controls. These results suggest that visual information contributes to contextual fear learning and that visual components of the context can be manipulated via LCD screens. Our approach offers a simple modification to contextual fear conditioning paradigms whereby the visual features of a context can be manipulated to better understand the factors that contribute to contextual fear discrimination and generalization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Reinforcement Learning Deficits in People with Schizophrenia Persist after Extended Trials

PubMed Central

Cicero, David C.; Martin, Elizabeth A.; Becker, Theresa M.; Kerns, John G.

2014-01-01

Previous research suggests that people with schizophrenia have difficulty learning from positive feedback and when learning needs to occur rapidly. However, they seem to have relatively intact learning from negative feedback when learning occurs gradually. Participants are typically given a limited amount of acquisition trials to learn the reward contingencies and then tested about what they learned. The current study examined whether participants with schizophrenia continue to display these deficits when given extra time to learn the contingences. Participants with schizophrenia and matched healthy controls completed the Probabilistic Selection Task, which measures positive and negative feedback learning separately. Participants with schizophrenia showed a deficit in learning from both positive and negative feedback. These reward learning deficits persisted even if people with schizophrenia are given extra time (up to 10 blocks of 60 trials) to learn the reward contingencies. These results suggest that the observed deficits cannot be attributed solely to slower learning and instead reflect a specific deficit in reinforcement learning. PMID:25172610

Reinforcement learning deficits in people with schizophrenia persist after extended trials.

PubMed

Cicero, David C; Martin, Elizabeth A; Becker, Theresa M; Kerns, John G

2014-12-30

Previous research suggests that people with schizophrenia have difficulty learning from positive feedback and when learning needs to occur rapidly. However, they seem to have relatively intact learning from negative feedback when learning occurs gradually. Participants are typically given a limited amount of acquisition trials to learn the reward contingencies and then tested about what they learned. The current study examined whether participants with schizophrenia continue to display these deficits when given extra time to learn the contingences. Participants with schizophrenia and matched healthy controls completed the Probabilistic Selection Task, which measures positive and negative feedback learning separately. Participants with schizophrenia showed a deficit in learning from both positive feedback and negative feedback. These reward learning deficits persisted even if people with schizophrenia are given extra time (up to 10 blocks of 60 trials) to learn the reward contingencies. These results suggest that the observed deficits cannot be attributed solely to slower learning and instead reflect a specific deficit in reinforcement learning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

Carbon Nanotube-Reinforced Thermotropic Liquid Crystal Polymer Nanocomposites

PubMed Central

Kim, Jun Young

2009-01-01

This paper focuses on the fabrication via simple melt blending of thermotropic liquid crystal polyester (TLCP) nanocomposites reinforced with a very small quantity of modified carbon nanotube (CNT) and the unique effects of the modified CNT on the physical properties of the nanocomposites. The thermal, mechanical, and rheological properties of modified CNT-reinforced TLCP nanocomposites are highly dependent on the uniform dispersion of CNT and the interactions between the CNT and TLCP, which can be enhanced by chemical modification of the CNT, providing a design guide of CNT-reinforced TLCP nanocomposites with great potential for industrial uses.

Reinforcement learning of periodical gaits in locomotion robots

NASA Astrophysics Data System (ADS)

Svinin, Mikhail; Yamada, Kazuyaki; Ushio, S.; Ueda, Kanji

1999-08-01

Emergence of stable gaits in locomotion robots is studied in this paper. A classifier system, implementing an instance- based reinforcement learning scheme, is used for sensory- motor control of an eight-legged mobile robot. Important feature of the classifier system is its ability to work with the continuous sensor space. The robot does not have a prior knowledge of the environment, its own internal model, and the goal coordinates. It is only assumed that the robot can acquire stable gaits by learning how to reach a light source. During the learning process the control system, is self-organized by reinforcement signals. Reaching the light source defines a global reward. Forward motion gets a local reward, while stepping back and falling down get a local punishment. Feasibility of the proposed self-organized system is tested under simulation and experiment. The control actions are specified at the leg level. It is shown that, as learning progresses, the number of the action rules in the classifier systems is stabilized to a certain level, corresponding to the acquired gait patterns.

Biases in probabilistic category learning in relation to social anxiety

PubMed Central

Abraham, Anna; Hermann, Christiane

2015-01-01

Instrumental learning paradigms are rarely employed to investigate the mechanisms underlying acquired fear responses in social anxiety. Here, we adapted a probabilistic category learning paradigm to assess information processing biases as a function of the degree of social anxiety traits in a sample of healthy individuals without a diagnosis of social phobia. Participants were presented with three pairs of neutral faces with differing probabilistic accuracy contingencies (A/B: 80/20, C/D: 70/30, E/F: 60/40). Upon making their choice, negative and positive feedback was conveyed using angry and happy faces, respectively. The highly socially anxious group showed a strong tendency to be more accurate at learning the probability contingency associated with the most ambiguous stimulus pair (E/F: 60/40). Moreover, when pairing the most positively reinforced stimulus or the most negatively reinforced stimulus with all the other stimuli in a test phase, the highly socially anxious group avoided the most negatively reinforced stimulus significantly more than the control group. The results are discussed with reference to avoidance learning and hypersensitivity to negative socially evaluative information associated with social anxiety. PMID:26347685

Surprise beyond prediction error

PubMed Central

Chumbley, Justin R; Burke, Christopher J; Stephan, Klaas E; Friston, Karl J; Tobler, Philippe N; Fehr, Ernst

2014-01-01

Surprise drives learning. Various neural “prediction error” signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and accounted for a host of potential SPE confounds. This new surprise signal was evident in ventral striatum, primary sensory cortex, frontal poles, and amygdala. We interpret these findings via a normative model of surprise. PMID:24700400

Optimal and Autonomous Control Using Reinforcement Learning: A Survey.

PubMed

Kiumarsi, Bahare; Vamvoudakis, Kyriakos G; Modares, Hamidreza; Lewis, Frank L

2018-06-01

This paper reviews the current state of the art on reinforcement learning (RL)-based feedback control solutions to optimal regulation and tracking of single and multiagent systems. Existing RL solutions to both optimal and control problems, as well as graphical games, will be reviewed. RL methods learn the solution to optimal control and game problems online and using measured data along the system trajectories. We discuss Q-learning and the integral RL algorithm as core algorithms for discrete-time (DT) and continuous-time (CT) systems, respectively. Moreover, we discuss a new direction of off-policy RL for both CT and DT systems. Finally, we review several applications.

Suppression of Striatal Prediction Errors by the Prefrontal Cortex in Placebo Hypoalgesia.

PubMed

Schenk, Lieven A; Sprenger, Christian; Onat, Selim; Colloca, Luana; Büchel, Christian

2017-10-04

Classical learning theories predict extinction after the discontinuation of reinforcement through prediction errors. However, placebo hypoalgesia, although mediated by associative learning, has been shown to be resistant to extinction. We tested the hypothesis that this is mediated by the suppression of prediction error processing through the prefrontal cortex (PFC). We compared pain modulation through treatment cues (placebo hypoalgesia, treatment context) with pain modulation through stimulus intensity cues (stimulus context) during functional magnetic resonance imaging in 48 male and female healthy volunteers. During acquisition, our data show that expectations are correctly learned and that this is associated with prediction error signals in the ventral striatum (VS) in both contexts. However, in the nonreinforced test phase, pain modulation and expectations of pain relief persisted to a larger degree in the treatment context, indicating that the expectations were not correctly updated in the treatment context. Consistently, we observed significantly stronger neural prediction error signals in the VS in the stimulus context compared with the treatment context. A connectivity analysis revealed negative coupling between the anterior PFC and the VS in the treatment context, suggesting that the PFC can suppress the expression of prediction errors in the VS. Consistent with this, a participant's conceptual views and beliefs about treatments influenced the pain modulation only in the treatment context. Our results indicate that in placebo hypoalgesia contextual treatment information engages prefrontal conceptual processes, which can suppress prediction error processing in the VS and lead to reduced updating of treatment expectancies, resulting in less extinction of placebo hypoalgesia. SIGNIFICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction when reinforcement is discontinued. This is thought to be mediated by prediction errors (i.e., the difference between expectations and outcome). Although reinforcement learning has been central in explaining placebo hypoalgesia, placebo hypoalgesic effects show little extinction and persist after the discontinuation of reinforcement. Our results support the idea that conceptual treatment beliefs bias the neural processing of expectations in a treatment context compared with a more stimulus-driven processing of expectations with stimulus intensity cues. We provide evidence that this is associated with the suppression of prediction error processing in the ventral striatum by the prefrontal cortex. This provides a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia. Copyright © 2017 the authors 0270-6474/17/379715-09$15.00/0.

Working Memory Contributions to Reinforcement Learning Impairments in Schizophrenia

PubMed Central

Brown, Jaime K.; Gold, James M.; Waltz, James A.; Frank, Michael J.

2014-01-01

Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia. PMID:25297101

Reconsideration of Serial Visual Reversal Learning in Octopus (Octopus vulgaris) from a Methodological Perspective

PubMed Central

Bublitz, Alexander; Weinhold, Severine R.; Strobel, Sophia; Dehnhardt, Guido; Hanke, Frederike D.

2017-01-01

Octopuses (Octopus vulgaris) are generally considered to possess extraordinary cognitive abilities including the ability to successfully perform in a serial reversal learning task. During reversal learning, an animal is presented with a discrimination problem and after reaching a learning criterion, the signs of the stimuli are reversed: the former positive becomes the negative stimulus and vice versa. If an animal improves its performance over reversals, it is ascribed advanced cognitive abilities. Reversal learning has been tested in octopus in a number of studies. However, the experimental procedures adopted in these studies involved pre-training on the new positive stimulus after a reversal, strong negative reinforcement or might have enabled secondary cueing by the experimenter. These procedures could have all affected the outcome of reversal learning. Thus, in this study, serial visual reversal learning was revisited in octopus. We trained four common octopuses (O. vulgaris) to discriminate between 2-dimensional stimuli presented on a monitor in a simultaneous visual discrimination task and reversed the signs of the stimuli each time the animals reached the learning criterion of ≥80% in two consecutive sessions. The animals were trained using operant conditioning techniques including a secondary reinforcer, a rod that was pushed up and down the feeding tube, which signaled the correctness of a response and preceded the subsequent primary reinforcement of food. The experimental protocol did not involve negative reinforcement. One animal completed four reversals and showed progressive improvement, i.e., it decreased its errors to criterion the more reversals it experienced. This animal developed a generalized response strategy. In contrast, another animal completed only one reversal, whereas two animals did not learn to reverse during the first reversal. In conclusion, some octopus individuals can learn to reverse in a visual task demonstrating behavioral flexibility even with a refined methodology. PMID:28223940

Learning in Mental Retardation: A Comprehensive Bibliography.

ERIC Educational Resources Information Center

Gardner, James M.; And Others

The bibliography on learning in mentally handicapped persons is divided into the following topic categories: applied behavior change, classical conditioning, discrimination, generalization, motor learning, reinforcement, verbal learning, and miscellaneous. An author index is included. (KW)

Asynchronous Gossip for Averaging and Spectral Ranking

NASA Astrophysics Data System (ADS)

Borkar, Vivek S.; Makhijani, Rahul; Sundaresan, Rajesh

2014-08-01

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

Reinforcement learning in professional basketball players

PubMed Central

Neiman, Tal; Loewenstein, Yonatan

2011-01-01

Reinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance. PMID:22146388

Universal effect of dynamical reinforcement learning mechanism in spatial evolutionary games

NASA Astrophysics Data System (ADS)

Zhang, Hai-Feng; Wu, Zhi-Xi; Wang, Bing-Hong

2012-06-01

One of the prototypical mechanisms in understanding the ubiquitous cooperation in social dilemma situations is the win-stay, lose-shift rule. In this work, a generalized win-stay, lose-shift learning model—a reinforcement learning model with dynamic aspiration level—is proposed to describe how humans adapt their social behaviors based on their social experiences. In the model, the players incorporate the information of the outcomes in previous rounds with time-dependent aspiration payoffs to regulate the probability of choosing cooperation. By investigating such a reinforcement learning rule in the spatial prisoner's dilemma game and public goods game, a most noteworthy viewpoint is that moderate greediness (i.e. moderate aspiration level) favors best the development and organization of collective cooperation. The generality of this observation is tested against different regulation strengths and different types of network of interaction as well. We also make comparisons with two recently proposed models to highlight the importance of the mechanism of adaptive aspiration level in supporting cooperation in structured populations.

From creatures of habit to goal-directed learners: Tracking the developmental emergence of model-based reinforcement learning

PubMed Central

Decker, Johannes H.; Otto, A. Ross; Daw, Nathaniel D.; Hartley, Catherine A.

2016-01-01

Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults, performed a sequential reinforcement-learning task that enables estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was evident in choice behavior across all age groups, evidence of a model-based strategy only emerged during adolescence and continued to increase into adulthood. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. PMID:27084852

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

PubMed Central

Franklin, Nicholas T; Frank, Michael J

2015-01-01

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments. DOI: http://dx.doi.org/10.7554/eLife.12029.001 PMID:26705698

Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

PubMed

Liu, Weirong; Zhuang, Peng; Liang, Hao; Peng, Jun; Huang, Zhiwu; Weirong Liu; Peng Zhuang; Hao Liang; Jun Peng; Zhiwu Huang; Liu, Weirong; Liang, Hao; Peng, Jun; Zhuang, Peng; Huang, Zhiwu

2018-06-01

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Research progress of microbial corrosion of reinforced concrete structure

NASA Astrophysics Data System (ADS)

Li, Shengli; Li, Dawang; Jiang, Nan; Wang, Dongwei

2011-04-01

Microbial corrosion of reinforce concrete structure is a new branch of learning. This branch deals with civil engineering , environment engineering, biology, chemistry, materials science and so on and is a interdisciplinary area. Research progress of the causes, research methods and contents of microbial corrosion of reinforced concrete structure is described. The research in the field is just beginning and concerted effort is needed to go further into the mechanism of reinforce concrete structure and assess the security and natural life of reinforce concrete structure under the special condition and put forward the protective methods.

Reinforcement learning with Marr.

PubMed

Niv, Yael; Langdon, Angela

2016-10-01

To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning-a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concluding that the path forward to their resolution calls for inspiration across levels, rather than a focus on mutual constraints.

Time-Extended Policies in Mult-Agent Reinforcement Learning

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Agogino, Adrian K.

2004-01-01

Reinforcement learning methods perform well in many domains where a single agent needs to take a sequence of actions to perform a task. These methods use sequences of single-time-step rewards to create a policy that tries to maximize a time-extended utility, which is a (possibly discounted) sum of these rewards. In this paper we build on our previous work showing how these methods can be extended to a multi-agent environment where each agent creates its own policy that works towards maximizing a time-extended global utility over all agents actions. We show improved methods for creating time-extended utilities for the agents that are both "aligned" with the global utility and "learnable." We then show how to crate single-time-step rewards while avoiding the pi fall of having rewards aligned with the global reward leading to utilities not aligned with the global utility. Finally, we apply these reward functions to the multi-agent Gridworld problem. We explicitly quantify a utility's learnability and alignment, and show that reinforcement learning agents using the prescribed reward functions successfully tradeoff learnability and alignment. As a result they outperform both global (e.g., team games ) and local (e.g., "perfectly learnable" ) reinforcement learning solutions by as much as an order of magnitude.

Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning.

PubMed

Mkrtchian, Anahit; Aylward, Jessica; Dayan, Peter; Roiser, Jonathan P; Robinson, Oliver J

2017-10-01

Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Variable Behavior and Repeated Learning in Two Mouse Strains: Developmental and Genetic Contributions.

PubMed

Arnold, Megan A; Newland, M Christopher

2018-06-16

Behavioral inflexibility is often assessed using reversal learning tasks, which require a relatively low degree of response variability. No studies have assessed sensitivity to reinforcement contingencies that specifically select highly variable response patterns in mice, let alone in models of neurodevelopmental disorders involving limited response variation. Operant variability and incremental repeated acquisition (IRA) were used to assess unique aspects of behavioral variability of two mouse strains: BALB/c, a model of some deficits in ASD, and C57Bl/6. On the operant variability task, BALB/c mice responded more repetitively during adolescence than C57Bl/6 mice when reinforcement did not require variability but responded more variably when reinforcement required variability. During IRA testing in adulthood, both strains acquired an unchanging, performance sequence equally well. Strain differences emerged, however, after novel learning sequences began alternating with the performance sequence: BALB/c mice substantially outperformed C57Bl/6 mice. Using litter-mate controls, it was found that adolescent experience with variability did not affect either learning or performance on the IRA task in adulthood. These findings constrain the use of BALB/c mice as a model of ASD, but once again reveal this strain is highly sensitive to reinforcement contingencies and they are fast and robust learners. Copyright © 2018. Published by Elsevier B.V.

Preventing Learned Helplessness.

ERIC Educational Resources Information Center

Hoy, Cheri

1986-01-01

To prevent learned helplessness in learning disabled students, teachers can share responsibilities with the students, train students to reinforce themselves for effort and self control, and introduce opportunities for changing counterproductive attitudes. (CL)

Relapse processes after the extinction of instrumental learning: Renewal, resurgence, and reacquisition

PubMed Central

Bouton, Mark E.; Winterbauer, Neil E.; Todd, Travis P.

2012-01-01

It is widely recognized that extinction (the procedure in which a Pavlovian conditioned stimulus or an instrumental action is repeatedly presented without its reinforcer) weakens behavior without erasing the original learning. Most of the experiments that support this claim have focused on several “relapse” effects that occur after Pavlovian extinction, which collectively suggest that the original learning is saved through extinction. However, although such effects do occur after instrumental extinction, they have not been explored there in as much detail. This article reviews recent research in our laboratory that has investigated three relapse effects that occur after the extinction of instrumental (operant) learning. In renewal, responding returns after extinction when the behavior is tested in a different context; in resurgence, responding recovers when a second response that has been reinforced during extinction of the first is itself put on extinction; and in rapid reacquisition, extinguished responding returns rapidly when the response is reinforced again. The results provide new insights into extinction and relapse, and are consistent with principles that have been developed to explain extinction and relapse as they occur after Pavlovian conditioning. Extinction of instrumental learning, like Pavlovian learning, involves new learning that is relatively dependent on the context for expression. PMID:22450305

Intelligent control and cooperation for mobile robots

NASA Astrophysics Data System (ADS)

Stingu, Petru Emanuel

The topic discussed in this work addresses the current research being conducted at the Automation & Robotics Research Institute in the areas of UAV quadrotor control and heterogenous multi-vehicle cooperation. Autonomy can be successfully achieved by a robot under the following conditions: the robot has to be able to acquire knowledge about the environment and itself, and it also has to be able to reason under uncertainty. The control system must react quickly to immediate challenges, but also has to slowly adapt and improve based on accumulated knowledge. The major contribution of this work is the transfer of the ADP algorithms from the purely theoretical environment to the complex real-world robotic platforms that work in real-time and in uncontrolled environments. Many solutions are adopted from those present in nature because they have been proven to be close to optimal in very different settings. For the control of a single platform, reinforcement learning algorithms are used to design suboptimal controllers for a class of complex systems that can be conceptually split in local loops with simpler dynamics and relatively weak coupling to the rest of the system. Optimality is enforced by having a global critic but the curse of dimensionality is avoided by using local actors and intelligent pre-processing of the information used for learning the optimal controllers. The system model is used for constructing the structure of the control system, but on top of that the adaptive neural networks that form the actors use the knowledge acquired during normal operation to get closer to optimal control. In real-world experiments, efficient learning is a strong requirement for success. This is accomplished by using an approximation of the system model to focus the learning for equivalent configurations of the state space. Due to the availability of only local data for training, neural networks with local activation functions are implemented. For the control of a formation of robots subjected to dynamic communication constraints, game theory is used in addition to reinforcement learning. The nodes maintain an extra set of state variables about all the other nodes that they can communicate to. The more important are trust and predictability. They are a way to incorporate knowledge acquired in the past into the control decisions taken by each node. The trust variable provides a simple mechanism for the implementation of reinforcement learning. For robot formations, potential field based control algorithms are used to generate the control commands. The formation structure changes due to the environment and due to the decisions of the nodes. It is a problem of building a graph and coalitions by having distributed decisions but still reaching an optimal behavior globally.

Economic decision-making in the ultimatum game by smokers.

PubMed

Takahashi, Taiki

2007-10-01

No study to date compared degrees of inequity aversion in economic decision-making in the ultimatum game between non-addictive and addictive reinforcers. The comparison is potentially important in neuroeconomics and reinforcement learning theory of addiction. We compared the degrees of inequity aversion in the ultimatum game between money and cigarettes in habitual smokers. Smokers avoided inequity in the ultimatum game more dramatically for money than for cigarettes; i.e., there was a "domain effect" in decision-making in the ultimatum game. Reward-processing neural activities in the brain for non-addictive and addictive reinforcers may be distinct and the insula activation due to cue-induced craving may conflict with unfair offer-induced insula activation. Future studies in neuroeconomics of addiction should employ game-theoretic decision tasks for elucidating reinforcement learning processes in dopaminergic neural circuits.

Reinforced two-step-ahead weight adjustment technique for online training of recurrent neural networks.

PubMed

Chang, Li-Chiu; Chen, Pin-An; Chang, Fi-John

2012-08-01

A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.

Simple green approach to reinforce natural rubber with bacterial cellulose nanofibers.

PubMed

Trovatti, Eliane; Carvalho, Antonio J F; Ribeiro, Sidney J L; Gandini, Alessandro

2013-08-12

Natural rubber (NR) is a renewable polymer with a wide range of applications, which is constantly tailored, further increasing its utilizations. The tensile strength is one of its most important properties susceptible of being enhanced by the simple incorporation of nanofibers. The preparation and characterization of natural-rubber based nanocomposites reinforced with bacterial cellulose (BC) and bacterial cellulose coated with polystyrene (BCPS), yielded high performance materials. The nanocomposites were prepared by a simple and green process, and characterized by tensile tests, dynamical mechanical analysis (DMA), scanning electron microscopy (SEM), and swelling experiments. The effect of the nanofiber content on morphology, static, and dynamic mechanical properties was also investigated. The results showed an increase in the mechanical properties, such as Young's modulus and tensile strength, even with modest nanofiber loadings.

The Identities Hidden in the Matching Laws, and Their Uses

ERIC Educational Resources Information Center

Thorne, David R.

2010-01-01

Various theoretical equations have been proposed to predict response rate as a function of the rate of reinforcement. If both the rate and probability of reinforcement are considered, a simple identity, defining equation, or "law" holds. This identity places algebraic constraints on the allowable forms of our mathematical models and can help…

Fun While Learning and Earning. A Look Into Chattanooga Public Schools' Token Reinforcement Program.

ERIC Educational Resources Information Center

Smith, William F.; Sanders, Frank J.

A token reinforcement program was used by the Piney Woods Research and Demonstration Center in Chattanooga, Tennessee. Children who were from economically deprived homes received tokens for positive behavior. The tokens were redeemable for recess privileges, ice cream, candy, and other such reinforcers. All tokens were spent on the day earned so…

The Curriculum-Faculty-Reinforcement Alignment and Its Effect on Learning Retention of Core Marketing Concepts of Marketing Capstone Students

ERIC Educational Resources Information Center

Raska, David; Keller, Eileen Weisenbach; Shaw, Doris

2014-01-01

Curriculum-Faculty-Reinforcement (CFR) alignment is an alignment between fundamental marketing concepts that are integral to the mastery of knowledge expected of our marketing graduates, their perceived importance by the faculty, and their level of reinforcement throughout core marketing courses required to obtain a marketing degree. This research…

Genetic Dissociation of Acquisition and Memory Strength in the Heat-Box Spatial Learning Paradigm in "Drosophila"

ERIC Educational Resources Information Center

Diegelmann, Soeren; Zars, Melissa; Zars, Troy

2006-01-01

Memories can have different strengths, largely dependent on the intensity of reinforcers encountered. The relationship between reinforcement and memory strength is evident in asymptotic memory curves, with the level of the asymptote related to the intensity of the reinforcer. Although this is likely a fundamental property of memory formation,…

Context-Outcome Associations Underlie Context-Switch Effects after Partial Reinforcement in Human Predictive Learning

ERIC Educational Resources Information Center

Moreno-Fernandez, Maria M.; Abad, Maria J. F.; Ramos-Alvarez, Manuel M.; Rosas, Juan M.

2011-01-01

Predictive value for continuously reinforced cues is affected by context changes when they are trained within a context in which a different cue undergoes partial reinforcement. An experiment was conducted with the goal of exploring the mechanisms underlying this context-switch effect. Human participants were trained in a predictive learning…

The Use of Reinforcement Procedures in Teaching Reading to Rural Culturally Deprived Children.

ERIC Educational Resources Information Center

Egeland, Byron

A group of culturally deprived children with severe reading and behavior problems was systematically given tangible reinforcers while learning to read. Twelve second-grade and 12 third-grade boys from a rural and lower socioeconomic background were taught reading with the use of tangible reinforcers (E group). Four similar control groups (C group)…

Cerebellar and prefrontal cortex contributions to adaptation, strategies, and reinforcement learning.

PubMed

Taylor, Jordan A; Ivry, Richard B

2014-01-01

Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. © 2014 Elsevier B.V. All rights reserved.

Temporally Coordinated Deep Brain Stimulation in the Dorsal and Ventral Striatum Synergistically Enhances Associative Learning.

PubMed

Katnani, Husam A; Patel, Shaun R; Kwon, Churl-Su; Abdel-Aziz, Samer; Gale, John T; Eskandar, Emad N

2016-01-04

The primate brain has the remarkable ability of mapping sensory stimuli into motor behaviors that can lead to positive outcomes. We have previously shown that during the reinforcement of visual-motor behavior, activity in the caudate nucleus is correlated with the rate of learning. Moreover, phasic microstimulation in the caudate during the reinforcement period was shown to enhance associative learning, demonstrating the importance of temporal specificity to manipulate learning related changes. Here we present evidence that extends upon our previous finding by demonstrating that temporally coordinated phasic deep brain stimulation across both the nucleus accumbens and caudate can further enhance associative learning. Monkeys performed a visual-motor associative learning task and received stimulation at time points critical to learning related changes. Resulting performance revealed an enhancement in the rate, ceiling, and reaction times of learning. Stimulation of each brain region alone or at different time points did not generate the same effect.

Cerebellar and Prefrontal Cortex Contributions to Adaptation, Strategies, and Reinforcement Learning

PubMed Central

Taylor, Jordan A.; Ivry, Richard B.

2014-01-01

Traditionally, motor learning has been studied as an implicit learning process, one in which movement errors are used to improve performance in a continuous, gradual manner. The cerebellum figures prominently in this literature given well-established ideas about the role of this system in error-based learning and the production of automatized skills. Recent developments have brought into focus the relevance of multiple learning mechanisms for sensorimotor learning. These include processes involving repetition, reinforcement learning, and strategy utilization. We examine these developments, considering their implications for understanding cerebellar function and how this structure interacts with other neural systems to support motor learning. Converging lines of evidence from behavioral, computational, and neuropsychological studies suggest a fundamental distinction between processes that use error information to improve action execution or action selection. While the cerebellum is clearly linked to the former, its role in the latter remains an open question. PMID:24916295

Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

NASA Technical Reports Server (NTRS)

Lee, Chuen-Chien

1991-01-01

Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

Self-Learning Intelligent Agents for Dynamic Traffic Routing on Transportation Networks

NASA Astrophysics Data System (ADS)

Sadek, Add; Basha, Nagi

Intelligent Transportation Systems (ITS) are designed to take advantage of recent advances in communications, electronics, and Information Technology in improving the efficiency and safety of transportation systems. Among the several ITS applications is the notion of Dynamic Traffic Routing (DTR), which involves generating "optimal" routing recommendations to drivers with the aim of maximizing network utilizing. In this paper, we demonstrate the feasibility of using a self-learning intelligent agent to solve the DTR problem to achieve traffic user equilibrium in a transportation network. The core idea is to deploy an agent to a simulation model of a highway. The agent then learns by itself by interacting with the simulation model. Once the agent reaches a satisfactory level of performance, it can then be deployed to the real-world, where it would continue to learn how to refine its control policies over time. To test this concept in this paper, the Cell Transmission Model (CTM) developed by Carlos Daganzo of the University of California at Berkeley is used to simulate a simple highway with two main alternative routes. With the model developed, a Reinforcement Learning Agent (RLA) is developed to learn how to best dynamically route traffic, so as to maximize the utilization of existing capacity. Preliminary results obtained from our experiments are promising. RL, being an adaptive online learning technique, appears to have a great potential for controlling a stochastic dynamic systems such as a transportation system. Furthermore, the approach is highly scalable and applicable to a variety of networks and roadways.

Stimulus discriminability may bias value-based probabilistic learning.

PubMed

Schutte, Iris; Slagter, Heleen A; Collins, Anne G E; Frank, Michael J; Kenemans, J Leon

2017-01-01

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

High and low temperatures have unequal reinforcing properties in Drosophila spatial learning.

PubMed

Zars, Melissa; Zars, Troy

2006-07-01

Small insects regulate their body temperature solely through behavior. Thus, sensing environmental temperature and implementing an appropriate behavioral strategy can be critical for survival. The fly Drosophila melanogaster prefers 24 degrees C, avoiding higher and lower temperatures when tested on a temperature gradient. Furthermore, temperatures above 24 degrees C have negative reinforcing properties. In contrast, we found that flies have a preference in operant learning experiments for a low-temperature-associated position rather than the 24 degrees C alternative in the heat-box. Two additional differences between high- and low-temperature reinforcement, i.e., temperatures above and below 24 degrees C, were found. Temperatures equally above and below 24 degrees C did not reinforce equally and only high temperatures supported increased memory performance with reversal conditioning. Finally, low- and high-temperature reinforced memories are similarly sensitive to two genetic mutations. Together these results indicate the qualitative meaning of temperatures below 24 degrees C depends on the dynamics of the temperatures encountered and that the reinforcing effects of these temperatures depend on at least some common genetic components. Conceptualizing these results using the Wolf-Heisenberg model of operant conditioning, we propose the maximum difference in experienced temperatures determines the magnitude of the reinforcement input to a conditioning circuit.

Exact solution of a modified El Farol's bar problem: Efficiency and the role of market impact

NASA Astrophysics Data System (ADS)

Marsili, Matteo; Challet, Damien; Zecchina, Riccardo

2000-06-01

We discuss a model of heterogeneous, inductive rational agents inspired by the El Farol Bar problem and the Minority Game. As in markets, agents interact through a collective aggregate variable - which plays a role similar to price - whose value is fixed by all of them. Agents follow a simple reinforcement-learning dynamics where the reinforcement, for each of their available strategies, is related to the payoff delivered by that strategy. We derive the exact solution of the model in the “thermodynamic” limit of infinitely many agents using tools of statistical physics of disordered systems. Our results show that the impact of agents on the market price plays a key role: even though price has a weak dependence on the behavior of each individual agent, the collective behavior crucially depends on whether agents account for such dependence or not. Remarkably, if the adaptive behavior of agents accounts even “infinitesimally” for this dependence they can, in a whole range of parameters, reduce global fluctuations by a finite amount. Both global efficiency and individual utility improve with respect to a “price taker” behavior if agents account for their market impact.

Instant transformation of learned repulsion into motivational "wanting".

PubMed

Robinson, Mike J F; Berridge, Kent C

2013-02-18

Learned cues for pleasant reward often elicit desire, which, in addicts, may become compulsive. According to the dominant view in addiction neuroscience and reinforcement modeling, such desires are the simple products of learning, coming from a past association with reward outcome. We demonstrate that cravings are more than merely the products of accumulated pleasure memories-even a repulsive learned cue for unpleasantness can become suddenly desired via the activation of mesocorticolimbic circuitry. Rats learned repulsion toward a Pavlovian cue (a briefly-inserted metal lever) that always predicted an unpleasant Dead Sea saltiness sensation. Yet, upon first reencounter in a novel sodium-depletion state to promote mesocorticolimbic reactivity (reflected by elevated Fos activation in ventral tegmentum, nucleus accumbens, ventral pallidum, and the orbitofrontal prefrontal cortex), the learned cue was instantly transformed into an attractive and powerful motivational magnet. Rats jumped and gnawed on the suddenly attractive Pavlovian lever cue, despite never having tasted intense saltiness as anything other than disgusting. Instant desire transformation of a learned cue contradicts views that Pavlovian desires are essentially based on previously learned values (e.g., prediction error or temporal difference models). Instead desire is recomputed at reencounter by integrating Pavlovian information with the current brain/physiological state. This powerful brain transformation reverses strong learned revulsion into avid attraction. When applied to addiction, related mesocorticolimbic transformations (e.g., drugs or neural sensitization) of cues for already-pleasant drug experiences could create even more intense cravings. This cue/state transformation helps define what it means to say that addiction hijacks brain limbic circuits of natural reward. Copyright © 2013 Elsevier Ltd. All rights reserved.

Feedback-related negativity is enhanced in adolescence during a gambling task with and without probabilistic reinforcement learning.

PubMed

Martínez-Velázquez, Eduardo S; Ramos-Loyo, Julieta; González-Garrido, Andrés A; Sequeira, Henrique

2015-01-21

Feedback-related negativity (FRN) is a negative deflection that appears around 250 ms after the gain or loss of feedback to chosen alternatives in a gambling task in frontocentral regions following outcomes. Few studies have reported FRN enhancement in adolescents compared with adults in a gambling task without probabilistic reinforcement learning, despite the fact that learning from positive or negative consequences is crucial for decision-making during adolescence. Therefore, the aim of the present research was to identify differences in FRN amplitude and latency between adolescents and adults on a gambling task with favorable and unfavorable probabilistic reinforcement learning conditions, in addition to a nonlearning condition with monetary gains and losses. Higher rate scores of high-magnitude choices during the final 30 trials compared with the first 30 trials were observed during the favorable condition, whereas lower rates were observed during the unfavorable condition in both groups. Higher FRN amplitude in all conditions and longer latency in the nonlearning condition were observed in adolescents compared with adults and in relation to losses. Results indicate that both the adolescents and the adults improved their performance in relation to positive and negative feedback. However, the FRN findings suggest an increased sensitivity to external feedback to losses in adolescents compared with adults, irrespective of the presence or absence of probabilistic reinforcement learning. These results reflect processing differences on the neural monitoring system and provide new perspectives on the dynamic development of an adolescent's brain.

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

NASA Technical Reports Server (NTRS)

Jani, Yashvant

1992-01-01

As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

Disrupted reinforcement learning and maladaptive behavior in women with a history of childhood sexual abuse: a high-density event-related potential study.

PubMed

Pechtel, Pia; Pizzagalli, Diego A

2013-05-01

Childhood sexual abuse (CSA) has been associated with psychopathology, particularly major depressive disorder (MDD), and high-risk behaviors. Despite the epidemiological data available, the mechanisms underlying these maladaptive outcomes remain poorly understood. We examined whether a history of CSA, particularly in conjunction with a past episode of MDD, is associated with behavioral and neural dysfunction in reinforcement learning, and whether such dysfunction is linked to maladaptive behavior. Participants completed a clinical evaluation and a probabilistic reinforcement task while 128-channel event-related potentials were recorded. Academic setting; participants recruited from the community. Fifteen women with a history of CSA and remitted MDD (CSA + rMDD), 16 women with remitted MDD with no history of CSA (rMDD), and 18 healthy women (controls). Three or more episodes of coerced sexual contact (mean [SD] duration, 3.00 [2.20] years) between the ages of 7 and 12 years by at least 1 male perpetrator. Participants' preference for choosing the most rewarded stimulus and avoiding the most punished stimulus was evaluated. The feedback-related negativity and error-related negativity-hypothesized to reflect activation in the anterior cingulate cortex-were used as electrophysiological indices of reinforcement learning. No group differences emerged in the acquisition of reinforcement contingencies. In trials requiring participants to rely partially or exclusively on previously rewarded information, the CSA + rMDD group showed (1) lower accuracy (relative to both controls and the rMDD group), (2) blunted electrophysiological differentiation between correct and incorrect responses (relative to controls), and (3) increased activation in the subgenual anterior cingulate cortex (relative to the rMDD group). A history of CSA was not associated with impairments in avoiding the most punished stimulus. Self-harm and suicidal behaviors correlated with poorer performance of previously rewarded, but not previously punished, trials. Irrespective of past MDD episodes, women with a history of CSA showed neural and behavioral deficits in utilizing previous reinforcement to optimize decision making in the absence of feedback (blunted "Go learning"). Although our study provides initial evidence for reward-specific deficits associated with CSA, future research is warranted to determine if disrupted positive reinforcement learning predicts high-risk behavior following CSA.

Bio-robots automatic navigation with graded electric reward stimulation based on Reinforcement Learning.

PubMed

Zhang, Chen; Sun, Chao; Gao, Liqiang; Zheng, Nenggan; Chen, Weidong; Zheng, Xiaoxiang

2013-01-01

Bio-robots based on brain computer interface (BCI) suffer from the lack of considering the characteristic of the animals in navigation. This paper proposed a new method for bio-robots' automatic navigation combining the reward generating algorithm base on Reinforcement Learning (RL) with the learning intelligence of animals together. Given the graded electrical reward, the animal e.g. the rat, intends to seek the maximum reward while exploring an unknown environment. Since the rat has excellent spatial recognition, the rat-robot and the RL algorithm can convergent to an optimal route by co-learning. This work has significant inspiration for the practical development of bio-robots' navigation with hybrid intelligence.

Emotion-based learning systems and the development of morality.

PubMed

Blair, R J R

2017-10-01

In this paper it is proposed that important components of moral development and moral judgment rely on two forms of emotional learning: stimulus-reinforcement and response-outcome learning. Data in support of this position will be primarily drawn from work with individuals with the developmental condition of psychopathy as well as fMRI studies with healthy individuals. Individuals with psychopathy show impairment on moral judgment tasks and a pronounced increased risk for instrumental antisocial behavior. It will be argued that these impairments are developmental consequences of impaired stimulus-aversive conditioning on the basis of distress cue reinforcers and response-outcome learning in individuals with this disorder. Copyright © 2017. Published by Elsevier B.V.

Collaborating Fuzzy Reinforcement Learning Agents

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

Earlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

PubMed

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

More Than the Sum of Its Parts: A Role for the Hippocampus in Configural Reinforcement Learning.

PubMed

Duncan, Katherine; Doll, Bradley B; Daw, Nathaniel D; Shohamy, Daphna

2018-05-02

People often perceive configurations rather than the elements they comprise, a bias that may emerge because configurations often predict outcomes. But how does the brain learn to associate configurations with outcomes and how does this learning differ from learning about individual elements? We combined behavior, reinforcement learning models, and functional imaging to understand how people learn to associate configurations of cues with outcomes. We found that configural learning depended on the relative predictive strength of elements versus configurations and was related to both the strength of BOLD activity and patterns of BOLD activity in the hippocampus. Configural learning was further related to functional connectivity between the hippocampus and nucleus accumbens. Moreover, configural learning was associated with flexible knowledge about associations and differential eye movements during choice. Together, this suggests that configural learning is associated with a distinct computational, cognitive, and neural profile that is well suited to support flexible and adaptive behavior. Copyright © 2018 Elsevier Inc. All rights reserved.

View Estimation Based on Value System

NASA Astrophysics Data System (ADS)

Takahashi, Yasutake; Shimada, Kouki; Asada, Minoru

Estimation of a caregiver's view is one of the most important capabilities for a child to understand the behavior demonstrated by the caregiver, that is, to infer the intention of behavior and/or to learn the observed behavior efficiently. We hypothesize that the child develops this ability in the same way as behavior learning motivated by an intrinsic reward, that is, he/she updates the model of the estimated view of his/her own during the behavior imitated from the observation of the behavior demonstrated by the caregiver based on minimizing the estimation error of the reward during the behavior. From this view, this paper shows a method for acquiring such a capability based on a value system from which values can be obtained by reinforcement learning. The parameters of the view estimation are updated based on the temporal difference error (hereafter TD error: estimation error of the state value), analogous to the way such that the parameters of the state value of the behavior are updated based on the TD error. Experiments with simple humanoid robots show the validity of the method, and the developmental process parallel to young children's estimation of its own view during the imitation of the observed behavior of the caregiver is discussed.

The Interaction of Temporal Generalization Gradients Predicts the Context Effect

ERIC Educational Resources Information Center

de Castro, Ana Catarina; Machado, Armando

2012-01-01

In a temporal double bisection task, animals learn two discriminations. In the presence of Red and Green keys, responses to Red are reinforced after 1-s samples and responses to Green are reinforced after 4-s samples; in the presence of Blue and Yellow keys, responses to Blue are reinforced after 4-s samples and responses to Yellow are reinforced…

Amygdala and Ventral Striatum Make Distinct Contributions to Reinforcement Learning.

PubMed

Costa, Vincent D; Dal Monte, Olga; Lucas, Daniel R; Murray, Elisabeth A; Averbeck, Bruno B

2016-10-19

Reinforcement learning (RL) theories posit that dopaminergic signals are integrated within the striatum to associate choices with outcomes. Often overlooked is that the amygdala also receives dopaminergic input and is involved in Pavlovian processes that influence choice behavior. To determine the relative contributions of the ventral striatum (VS) and amygdala to appetitive RL, we tested rhesus macaques with VS or amygdala lesions on deterministic and stochastic versions of a two-arm bandit reversal learning task. When learning was characterized with an RL model relative to controls, amygdala lesions caused general decreases in learning from positive feedback and choice consistency. By comparison, VS lesions only affected learning in the stochastic task. Moreover, the VS lesions hastened the monkeys' choice reaction times, which emphasized a speed-accuracy trade-off that accounted for errors in deterministic learning. These results update standard accounts of RL by emphasizing distinct contributions of the amygdala and VS to RL. Published by Elsevier Inc.

Amygdala and ventral striatum make distinct contributions to reinforcement learning

PubMed Central

Costa, Vincent D.; Monte, Olga Dal; Lucas, Daniel R.; Murray, Elisabeth A.; Averbeck, Bruno B.

2016-01-01

Summary Reinforcement learning (RL) theories posit that dopaminergic signals are integrated within the striatum to associate choices with outcomes. Often overlooked is that the amygdala also receives dopaminergic input and is involved in Pavlovian processes that influence choice behavior. To determine the relative contributions of the ventral striatum (VS) and amygdala to appetitive RL we tested rhesus macaques with VS or amygdala lesions on deterministic and stochastic versions of a two-arm bandit reversal learning task. When learning was characterized with a RL model relative to controls, amygdala lesions caused general decreases in learning from positive feedback and choice consistency. By comparison, VS lesions only affected learning in the stochastic task. Moreover, the VS lesions hastened the monkeys’ choice reaction times, which emphasized a speed-accuracy tradeoff that accounted for errors in deterministic learning. These results update standard accounts of RL by emphasizing distinct contributions of the amygdala and VS to RL. PMID:27720488

Working memory contributions to reinforcement learning impairments in schizophrenia.

PubMed

Collins, Anne G E; Brown, Jaime K; Gold, James M; Waltz, James A; Frank, Michael J

2014-10-08

Previous research has shown that patients with schizophrenia are impaired in reinforcement learning tasks. However, behavioral learning curves in such tasks originate from the interaction of multiple neural processes, including the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal cortex-dependent cognitive strategies involving working memory (WM). Thus, it is unclear which specific system induces impairments in schizophrenia. We recently developed a task and computational model allowing us to separately assess the roles of RL (slow, cumulative learning) mechanisms versus WM (fast but capacity-limited) mechanisms in healthy adult human subjects. Here, we used this task to assess patients' specific sources of impairments in learning. In 15 separate blocks, subjects learned to pick one of three actions for stimuli. The number of stimuli to learn in each block varied from two to six, allowing us to separate influences of capacity-limited WM from the incremental RL system. As expected, both patients (n = 49) and healthy controls (n = 36) showed effects of set size and delay between stimulus repetitions, confirming the presence of working memory effects. Patients performed significantly worse than controls overall, but computational model fits and behavioral analyses indicate that these deficits could be entirely accounted for by changes in WM parameters (capacity and reliability), whereas RL processes were spared. These results suggest that the working memory system contributes strongly to learning impairments in schizophrenia. Copyright © 2014 the authors 0270-6474/14/3413747-10$15.00/0.

Selection Dynamics in Joint Matching to Rate and Magnitude of Reinforcement

ERIC Educational Resources Information Center

McDowell, J. J.; Popa, Andrei; Calvin, Nicholas T.

2012-01-01

Virtual organisms animated by a selectionist theory of behavior dynamics worked on concurrent random interval schedules where both the rate and magnitude of reinforcement were varied. The selectionist theory consists of a set of simple rules of selection, recombination, and mutation that act on a population of potential behaviors by means of a…

Interacting with an artificial partner: modeling the role of emotional aspects.

PubMed

Cattinelli, Isabella; Goldwurm, Massimiliano; Borghese, N Alberto

2008-12-01

In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.

Functional Contour-following via Haptic Perception and Reinforcement Learning.

PubMed

Hellman, Randall B; Tekin, Cem; van der Schaar, Mihaela; Santos, Veronica J

2018-01-01

Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic perception and decision-making for a haptics-driven, functional contour-following task: the closure of a ziplock bag. This task is challenging for robots because the bag is deformable, transparent, and visually occluded by artificial fingertip sensors that are also compliant. A deep neural net classifier was trained to estimate the state of a zipper within a robot's pinch grasp. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards by balancing exploration versus exploitation of the state-action space. The C-MAB learner outperformed a benchmark Q-learner by more efficiently exploring the state-action space while learning a hard-to-code task. The learned C-MAB policy was tested with novel ziplock bag scenarios and contours (wire, rope). Importantly, this work contributes to the development of reinforcement learning approaches that account for limited resources such as hardware life and researcher time. As robots are used to perform complex, physically interactive tasks in unstructured or unmodeled environments, it becomes important to develop methods that enable efficient and effective learning with physical testbeds.

Quantum reinforcement learning.

PubMed

Dong, Daoyi; Chen, Chunlin; Li, Hanxiong; Tarn, Tzyh-Jong

2008-10-01

The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.

Ontogeny of passive avoidance learning in domestic chicks: punishment of key-peck and running responses.

PubMed

Mattingly, B A; Zolman, J F

1980-08-01

The effect of the number of prepunishment acquisition trials on the age dependency of passive avoidance (PA) learning of the Vantress X Arbor Acre chick was determined in both key-peck and runway tests. In nine experiments, 1- and 4-day-old chicks were first trained to respond for heat reward, and then, following a variable number of reinforced acquisition trials, the chicks' responses were punished with aversive wing shocks. The major finding of these experiments was that the age dependency of PA learning of the young chick is related specifically to the number of reinforced training trials given prior to PA testing. When a large number of prepunishment acquisition trials were given, 1-day-old chicks learned as quickly as 4-day-old chicks to withhold responding when punished. However, when only a few acquisition trials preceded PA testing, 1-day-old chicks showed significantly less response suppression than 4-day-old chicks. These acquisition effects indicate that the age-dependent changes in PA learning of the chick are not solely due to developmental changes in general inhibitory ability. Rather, these PA results suggest that the 1-day-old chick, compared with the 4-day-old chick, is deficient in learning, or detecting changes in, stimulus- and/or response-reinforcement contingencies.

Deficient reinforcement learning in medial frontal cortex as a model of dopamine-related motivational deficits in ADHD.

PubMed

Silvetti, Massimo; Wiersema, Jan R; Sonuga-Barke, Edmund; Verguts, Tom

2013-10-01

Attention Deficit/Hyperactivity Disorder (ADHD) is a pathophysiologically complex and heterogeneous condition with both cognitive and motivational components. We propose a novel computational hypothesis of motivational deficits in ADHD, drawing together recent evidence on the role of anterior cingulate cortex (ACC) and associated mesolimbic dopamine circuits in both reinforcement learning and ADHD. Based on findings of dopamine dysregulation and ACC involvement in ADHD we simulated a lesion in a previously validated computational model of ACC (Reward Value and Prediction Model, RVPM). We explored the effects of the lesion on the processing of reinforcement signals. We tested specific behavioral predictions about the profile of reinforcement-related deficits in ADHD in three experimental contexts; probability tracking task, partial and continuous reward schedules, and immediate versus delayed rewards. In addition, predictions were made at the neurophysiological level. Behavioral and neurophysiological predictions from the RVPM-based lesion-model of motivational dysfunction in ADHD were confirmed by data from previously published studies. RVPM represents a promising model of ADHD reinforcement learning suggesting that ACC dysregulation might play a role in the pathogenesis of motivational deficits in ADHD. However, more behavioral and neurophysiological studies are required to test core predictions of the model. In addition, the interaction with different brain networks underpinning other aspects of ADHD neuropathology (i.e., executive function) needs to be better understood. Copyright © 2013 Elsevier Ltd. All rights reserved.

Robotic action acquisition with cognitive biases in coarse-grained state space.

PubMed

Uragami, Daisuke; Kohno, Yu; Takahashi, Tatsuji

2016-07-01

Some of the authors have previously proposed a cognitively inspired reinforcement learning architecture (LS-Q) that mimics cognitive biases in humans. LS-Q adaptively learns under uniform, coarse-grained state division and performs well without parameter tuning in a giant-swing robot task. However, these results were shown only in simulations. In this study, we test the validity of the LS-Q implemented in a robot in a real environment. In addition, we analyze the learning process to elucidate the mechanism by which the LS-Q adaptively learns under the partially observable environment. We argue that the LS-Q may be a versatile reinforcement learning architecture, which is, despite its simplicity, easily applicable and does not require well-prepared settings. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Reinforcement Learning for Weakly-Coupled MDPs and an Application to Planetary Rover Control

NASA Technical Reports Server (NTRS)

Bernstein, Daniel S.; Zilberstein, Shlomo

2003-01-01

Weakly-coupled Markov decision processes can be decomposed into subprocesses that interact only through a small set of bottleneck states. We study a hierarchical reinforcement learning algorithm designed to take advantage of this particular type of decomposability. To test our algorithm, we use a decision-making problem faced by autonomous planetary rovers. In this problem, a Mars rover must decide which activities to perform and when to traverse between science sites in order to make the best use of its limited resources. In our experiments, the hierarchical algorithm performs better than Q-learning in the early stages of learning, but unlike Q-learning it converges to a suboptimal policy. This suggests that it may be advantageous to use the hierarchical algorithm when training time is limited.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology.

PubMed

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M; Lara, Juan A; Lizcano, David

2017-01-19

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency.

A Reinforcement Learning Model Equipped with Sensors for Generating Perception Patterns: Implementation of a Simulated Air Navigation System Using ADS-B (Automatic Dependent Surveillance-Broadcast) Technology

PubMed Central

Álvarez de Toledo, Santiago; Anguera, Aurea; Barreiro, José M.; Lara, Juan A.; Lizcano, David

2017-01-01

Over the last few decades, a number of reinforcement learning techniques have emerged, and different reinforcement learning-based applications have proliferated. However, such techniques tend to specialize in a particular field. This is an obstacle to their generalization and extrapolation to other areas. Besides, neither the reward-punishment (r-p) learning process nor the convergence of results is fast and efficient enough. To address these obstacles, this research proposes a general reinforcement learning model. This model is independent of input and output types and based on general bioinspired principles that help to speed up the learning process. The model is composed of a perception module based on sensors whose specific perceptions are mapped as perception patterns. In this manner, similar perceptions (even if perceived at different positions in the environment) are accounted for by the same perception pattern. Additionally, the model includes a procedure that statistically associates perception-action pattern pairs depending on the positive or negative results output by executing the respective action in response to a particular perception during the learning process. To do this, the model is fitted with a mechanism that reacts positively or negatively to particular sensory stimuli in order to rate results. The model is supplemented by an action module that can be configured depending on the maneuverability of each specific agent. The model has been applied in the air navigation domain, a field with strong safety restrictions, which led us to implement a simulated system equipped with the proposed model. Accordingly, the perception sensors were based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology, which is described in this paper. The results were quite satisfactory, and it outperformed traditional methods existing in the literature with respect to learning reliability and efficiency. PMID:28106849

Dopaminergic Contributions to Vocal Learning

PubMed Central

Hoffmann, Lukas A.; Saravanan, Varun; Wood, Alynda N.; He, Li

2016-01-01

Although the brain relies on auditory information to calibrate vocal behavior, the neural substrates of vocal learning remain unclear. Here we demonstrate that lesions of the dopaminergic inputs to a basal ganglia nucleus in a songbird species (Bengalese finches, Lonchura striata var. domestica) greatly reduced the magnitude of vocal learning driven by disruptive auditory feedback in a negative reinforcement task. These lesions produced no measureable effects on the quality of vocal performance or the amount of song produced. Our results suggest that dopaminergic inputs to the basal ganglia selectively mediate reinforcement-driven vocal plasticity. In contrast, dopaminergic lesions produced no measurable effects on the birds' ability to restore song acoustics to baseline following the cessation of reinforcement training, suggesting that different forms of vocal plasticity may use different neural mechanisms. SIGNIFICANCE STATEMENT During skill learning, the brain relies on sensory feedback to improve motor performance. However, the neural basis of sensorimotor learning is poorly understood. Here, we investigate the role of the neurotransmitter dopamine in regulating vocal learning in the Bengalese finch, a songbird with an extremely precise singing behavior that can nevertheless be reshaped dramatically by auditory feedback. Our findings show that reduction of dopamine inputs to a region of the songbird basal ganglia greatly impairs vocal learning but has no detectable effect on vocal performance. These results suggest a specific role for dopamine in regulating vocal plasticity. PMID:26888928

Characteristics of implicit chaining in cotton-top tamarins (Saguinus oedipus).

PubMed

Locurto, Charles; Gagne, Matthew; Nutile, Lauren

2010-07-01

In human cognition there has been considerable interest in observing the conditions under which subjects learn material without explicit instructions to learn. In the present experiments, we adapted this issue to nonhumans by asking what subjects learn in the absence of explicit reinforcement for correct responses. Two experiments examined the acquisition of sequence information by cotton-top tamarins (Saguinus oedipus) when such learning was not demanded by the experimental contingencies. An implicit chaining procedure was used in which visual stimuli were presented serially on a touchscreen. Subjects were required to touch one stimulus to advance to the next stimulus. Stimulus presentations followed a pattern, but learning the pattern was not necessary for reinforcement. In Experiment 1 the chain consisted of five different visual stimuli that were presented in the same order on each trial. Each stimulus could occur at any one of six touchscreen positions. In Experiment 2 the same visual element was presented serially in the same five locations on each trial, thereby allowing a behavioral pattern to be correlated with the visual pattern. In this experiment two new tests, a Wild-Card test and a Running-Start test, were used to assess what was learned in this procedure. Results from both experiments indicated that tamarins acquired more information from an implicit chain than was required by the contingencies of reinforcement. These results contribute to the developing literature on nonhuman analogs of implicit learning.

Dynamics of market structure driven by the degree of consumer’s rationality

NASA Astrophysics Data System (ADS)

Yanagita, Tatsuo; Onozaki, Tamotsu

2010-03-01

We study a simple model of market share dynamics with boundedly rational consumers and firms interacting with each other. As the number of consumers is large, we employ a statistical description to represent firms’ distribution of consumer share, which is characterized by a single parameter representing how rationally the mass of consumers pursue higher utility. As the boundedly rational firm does not know the shape of demand function it faces, it revises production and price so as to raise its profit with the aid of a simple reinforcement learning rule. Simulation results show that (1) three phases of market structure, i.e. the uniform share phase, the oligopolistic phase, and the monopolistic phase, appear depending upon how rational consumers are, and (2) in an oligopolistic phase, the market share distribution of firms follows Zipf’s law and the growth-rate distribution of firms follows Gibrat’s law, and (3) an oligopolistic phase is the best state of market in terms of consumers’ utility but brings the minimum profit to the firms because of severe competition based on the moderate rationality of consumers.

Understanding Optimal Decision-Making

DTIC Science & Technology

2015-06-01

Task (IGT) (Bechara, Damasio, Damasio, & Anderson,1994), a very common test of reinforcement learning that has been used in hundreds of psychology ... psychology task that elicits reinforcement learning (Bechara et al., 1994) and has been used in hundreds of studies (Krain et al., 2006). Subjects...34) 70 # LatByTrial<- LatByTrial+geom_line(data=player,aes(x=trial,y=ewma),linetype=1, colour ="grey8 8") # LatByTrial<- LatByTrial+geom_point

Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma.

PubMed

Harper, Marc; Knight, Vincent; Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner's Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.

Unaltered radial maze performance and brain acetylcholine of the endothelial nitric oxide synthase knockout mouse.

PubMed

Dere, E; Frisch, C; De Souza Silva, M A; Gödecke, A; Schrader, J; Huston, J P

2001-01-01

Proceeding from previous findings of a beneficial effect of endothelial nitric oxide synthase (eNOS) gene inactivation on negatively reinforced water maze performance, we asked whether this improvement in place learning capacities also holds for a positively reinforced radial maze task. Unlike its beneficial effects on the water maze task, eNOS gene inactivation did not facilitate radial maze performance. The acquisition performance over the days of place learning did not differ between eNOS knockout (eNOS-/-) and wild-type mice (eNOS+/+). eNOS-/- mice displayed a slight and eNOS+/+ mice a more severe working memory deficit in the place learning version of the radial maze compared to the genetic background C57BL/6 strain. Possible differential effects of eNOS inactivation, related to differences in reinforcement contingencies between the Morris water maze and radial maze tasks, behavioral strategy requirements, or to different emotional and physiological concomitants inherent in the two tasks are discussed. These task-unique characteristics might be differentially affected by the reported anxiogenic and hypertensional effects of eNOS gene inactivation. Post-mortem determination of acetylcholine concentrations in diverse brain structures revealed that acetylcholine and choline contents were not different between eNOS-/- and eNOS+/+ mice, but were increased in eNOS+/+ mice compared to C57BL/6 mice in the frontal cortex. Our findings demonstrate that phenotyping of learning and memory capacities should not rely on one learning task only, but should include tasks employing both negative and positive reinforcement contingencies in order to allow valid statements regarding differences in learning capacities between rodent strains.

Modeling the Violation of Reward Maximization and Invariance in Reinforcement Schedules

PubMed Central

La Camera, Giancarlo; Richmond, Barry J.

2008-01-01

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as “schedule length effect”). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: “framing,” wherein equivalent options are treated differently depending on the context in which they are presented, and the “sunk cost” effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys. PMID:18688266

Modeling the violation of reward maximization and invariance in reinforcement schedules.

PubMed

La Camera, Giancarlo; Richmond, Barry J

2008-08-08

It is often assumed that animals and people adjust their behavior to maximize reward acquisition. In visually cued reinforcement schedules, monkeys make errors in trials that are not immediately rewarded, despite having to repeat error trials. Here we show that error rates are typically smaller in trials equally distant from reward but belonging to longer schedules (referred to as "schedule length effect"). This violates the principles of reward maximization and invariance and cannot be predicted by the standard methods of Reinforcement Learning, such as the method of temporal differences. We develop a heuristic model that accounts for all of the properties of the behavior in the reinforcement schedule task but whose predictions are not different from those of the standard temporal difference model in choice tasks. In the modification of temporal difference learning introduced here, the effect of schedule length emerges spontaneously from the sensitivity to the immediately preceding trial. We also introduce a policy for general Markov Decision Processes, where the decision made at each node is conditioned on the motivation to perform an instrumental action, and show that the application of our model to the reinforcement schedule task and the choice task are special cases of this general theoretical framework. Within this framework, Reinforcement Learning can approach contextual learning with the mixture of empirical findings and principled assumptions that seem to coexist in the best descriptions of animal behavior. As examples, we discuss two phenomena observed in humans that often derive from the violation of the principle of invariance: "framing," wherein equivalent options are treated differently depending on the context in which they are presented, and the "sunk cost" effect, the greater tendency to continue an endeavor once an investment in money, effort, or time has been made. The schedule length effect might be a manifestation of these phenomena in monkeys.

Operant licking for intragastric sugar infusions: differential reinforcing actions of glucose, sucrose and fructose in mice

PubMed Central

Sclafani, Anthony; Ackroff, Karen

2015-01-01

Intragastric (IG) flavor conditioning studies in rodents indicate that isocaloric sugar infusions differ in their reinforcing actions, with glucose and sucrose more potent than fructose. Here we determined if the sugars also differ in their ability to maintain operant self-administration by licking an empty spout for IG infusions. Food-restricted C57BL/6J mice were trained 1 h/day to lick a food-baited spout, which triggered IG infusions of 16% sucrose. In testing, the mice licked an empty spout, which triggered IG infusions of different sugars. Mice shifted from sucrose to 16% glucose increased dry licking, whereas mice shifted to 16% fructose rapidly reduced licking to low levels. Other mice shifted from sucrose to IG water reduced licking more slowly but reached the same low levels. Thus IG fructose, like water, is not reinforcing to hungry mice. The more rapid decline in licking induced by fructose may be due to the sugar's satiating effects. Further tests revealed that the Glucose mice increased their dry licking when shifted from 16% to 8% glucose, and reduced their dry licking when shifted to 32% glucose. This may reflect caloric regulation and/or differences in satiation. The Glucose mice did not maintain caloric intake when tested with different sugars. They self-infused less sugar when shifted from 16% glucose to 16% sucrose, and even more so when shifted to 16% fructose. Reduced sucrose self-administration may occur because the fructose component of the disaccharide reduces its reinforcing potency. FVB mice also reduced operant licking when tested with 16% fructose, yet learned to prefer a flavor paired with IG fructose. These data indicate that sugars differ substantially in their ability to support IG self-administration and flavor preference learning. The same post-oral reinforcement process appears to mediate operant licking and flavor learning, although flavor learning provides a more sensitive measure of sugar reinforcement. PMID:26485294

Optimal control in microgrid using multi-agent reinforcement learning.

PubMed

Li, Fu-Dong; Wu, Min; He, Yong; Chen, Xin

2012-11-01

This paper presents an improved reinforcement learning method to minimize electricity costs on the premise of satisfying the power balance and generation limit of units in a microgrid with grid-connected mode. Firstly, the microgrid control requirements are analyzed and the objective function of optimal control for microgrid is proposed. Then, a state variable "Average Electricity Price Trend" which is used to express the most possible transitions of the system is developed so as to reduce the complexity and randomicity of the microgrid, and a multi-agent architecture including agents, state variables, action variables and reward function is formulated. Furthermore, dynamic hierarchical reinforcement learning, based on change rate of key state variable, is established to carry out optimal policy exploration. The analysis shows that the proposed method is beneficial to handle the problem of "curse of dimensionality" and speed up learning in the unknown large-scale world. Finally, the simulation results under JADE (Java Agent Development Framework) demonstrate the validity of the presented method in optimal control for a microgrid with grid-connected mode. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

From Creatures of Habit to Goal-Directed Learners: Tracking the Developmental Emergence of Model-Based Reinforcement Learning.

PubMed

Decker, Johannes H; Otto, A Ross; Daw, Nathaniel D; Hartley, Catherine A

2016-06-01

Theoretical models distinguish two decision-making strategies that have been formalized in reinforcement-learning theory. A model-based strategy leverages a cognitive model of potential actions and their consequences to make goal-directed choices, whereas a model-free strategy evaluates actions based solely on their reward history. Research in adults has begun to elucidate the psychological mechanisms and neural substrates underlying these learning processes and factors that influence their relative recruitment. However, the developmental trajectory of these evaluative strategies has not been well characterized. In this study, children, adolescents, and adults performed a sequential reinforcement-learning task that enabled estimation of model-based and model-free contributions to choice. Whereas a model-free strategy was apparent in choice behavior across all age groups, a model-based strategy was absent in children, became evident in adolescents, and strengthened in adults. These results suggest that recruitment of model-based valuation systems represents a critical cognitive component underlying the gradual maturation of goal-directed behavior. © The Author(s) 2016.

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

NASA Technical Reports Server (NTRS)

Tran, Loc; Cross, Charles; Montague, Gilbert; Motter, Mark; Neilan, James; Qualls, Garry; Rothhaar, Paul; Trujillo, Anna; Allen, B. Danette

2015-01-01

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Moment redistribution in continuous reinforced concrete beams strengthened with carbon-fiber-reinforced polymer laminates

NASA Astrophysics Data System (ADS)

Aiello, M. A.; Valente, L.; Rizzo, A.

2007-09-01

The results of tests on continuous steel-fiber-reinforced concrete (RC) beams, with and without an external strengthening, are presented. The internal flexural steel reinforcement was designed so that to allow steel yielding before the collapse of the beams. To prevent the shear failure, steel stirrups were used. The tests also included two nonstrengthened control beams; the other specimens were strengthened with different configurations of externally bonded carbon-fiber-reinforced polymer (CFRP) laminates. In order to prevent the premature failure from delamination of the CFRP strengthening, a wrapping was also applied. The experimental results obtained show that it is possible to achieve a sufficient degree of moment redistribution if the strengthening configuration is chosen properly, confirming the results provided by two simple numerical models.

[Preparation of carbon fiber reinforced fluid type resin denture (author's transl)].

PubMed

Kasuga, H; Sato, H; Nakabayashi, N

1980-01-01

Transverse strength of cured fluid resins is weaker than that of the heat cured. We have studied to improve the mechanical strength of self-cured acrylic resin by application of carbon fibers as reinforcement and simple methods which must be acceptable for technicians are proposed. A cloth type carbon fiber was the best reinforcement among studied carbon fibers such as chopped or mat. The chopped fibers were difficult to mix homogeneously with fluid resins and effectiveness of the reinforcement was low. Breaking often occurred at the interface between the reinforcement and resin in the cases of mat which gave defects to the test specimens. To prepare reinforced denture, the cloth was trimmed on the master cast after removal of wax and the prepreg was formed with the alginate impression on the cast by Palapress and the cloth. Other steps were same as the usual fluid resin.

Stimulating Deep Learning Using Active Learning Techniques

ERIC Educational Resources Information Center

Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

2016-01-01

When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

CENTRAL REINFORCING EFFECTS OF ETHANOL ARE BLOCKED BY CATALASE INHIBITION

PubMed Central

Nizhnikov, Michael Edward; Molina, Juan Carlos; Spear, Norman

2007-01-01

Recent studies have systematically indicated that newborn rats are highly sensitive to ethanol’s positive reinforcing effects. Central administrations of ethanol (25–200 mg %) associated with an olfactory conditioned stimulus (CS) promote subsequent conditioned approach to the CS as evaluated through the newborn’s response to a surrogate nipple scented with the CS. It has been shown that ethanol’s first metabolite, acetaldehyde, exerts significant reinforcing effects in the central nervous system. A significant amount of acetaldehyde is derived from ethanol metabolism via the catalase system. In newborn rats catalase levels are particularly high in several brain structures. The present study tested the effect of catalase inhibition on central ethanol reinforcement. In the first experiment, pups experienced lemon odor either paired or unpaired with intracisternal (i.c.) administrations of 100 mg% ethanol. Half of the animals corresponding to each learning condition were pretreated with i.c. administrations of either physiological saline or a catalase inhibitor (sodium-azide). Catalase inhibition completely suppressed ethanol reinforcement in paired groups without affecting responsiveness to the CS during conditioning or responding by unpaired control groups. A second experiment tested whether these effects were specific to ethanol reinforcement or due instead to general impairment in learning and expression capabilities. Central administration of an endogenous kappa opioid receptor agonist (dynorphin A-13) was employed as an alternative source of reinforcement. Inhibition of the catalase system had no effect on the reinforcing properties of dynorphin. The present results support the hypothesis that ethanol metabolism regulated by the catalase system plays a critical role in determination of ethanol reinforcement in newborn rats. PMID:17980789

The involvement of model-based but not model-free learning signals during observational reward learning in the absence of choice.

PubMed

Dunne, Simon; D'Souza, Arun; O'Doherty, John P

2016-06-01

A major open question is whether computational strategies thought to be used during experiential learning, specifically model-based and model-free reinforcement learning, also support observational learning. Furthermore, the question of how observational learning occurs when observers must learn about the value of options from observing outcomes in the absence of choice has not been addressed. In the present study we used a multi-armed bandit task that encouraged human participants to employ both experiential and observational learning while they underwent functional magnetic resonance imaging (fMRI). We found evidence for the presence of model-based learning signals during both observational and experiential learning in the intraparietal sulcus. However, unlike during experiential learning, model-free learning signals in the ventral striatum were not detectable during this form of observational learning. These results provide insight into the flexibility of the model-based learning system, implicating this system in learning during observation as well as from direct experience, and further suggest that the model-free reinforcement learning system may be less flexible with regard to its involvement in observational learning. Copyright © 2016 the American Physiological Society.

Economic concepts for the analysis of behavior

PubMed Central

Hursh, Steven R.

1980-01-01

A review of the relationship between schedule of reinforcement, response rate, and choice suggests that certain unifying concepts from economics can contribute to a more complete science of behavior. Four points are made: 1) a behavioral experiment is an economic system and its characteristics—open or closed—can strongly determine the results; 2) reinforcers can be distinguished by a functional property called elasticity; 3) reinforcers may interact as complements as well as substitutes; 4) no simple choice rule, such as strict matching, can account for all choice behavior. PMID:16812188

Fused Deposition Technique for Continuous Fiber Reinforced Thermoplastic

NASA Astrophysics Data System (ADS)

Bettini, Paolo; Alitta, Gianluca; Sala, Giuseppe; Di Landro, Luca

2017-02-01

A simple technique for the production of continuous fiber reinforced thermoplastic by fused deposition modeling, which involves a common 3D printer with quite limited modifications, is presented. An adequate setting of processing parameters and deposition path allows to obtain components with well-enhanced mechanical characteristics compared to conventional 3D printed items. The most relevant problems related to the simultaneous feeding of fibers and polymer are discussed. The properties of obtained aramid fiber reinforced polylactic acid (PLA) in terms of impregnation quality and of mechanical response are measured.

The usage of carbon fiber reinforcement polymer and glass fiber reinforcement polymer for retrofit technology building

NASA Astrophysics Data System (ADS)

Tarigan, Johannes; Meka, Randi; Nursyamsi

2018-03-01

Fiber Reinforcement Polymer has been used as a material technology since the 1970s in Europe. Fiber Reinforcement Polymer can reinforce the structure externally, and used in many types of buildings like beams, columns, and slabs. It has high tensile strength. Fiber Reinforcement Polymer also has high rigidity and strength. The profile of Fiber Reinforcement Polymer is thin and light, installation is simple to conduct. One of Fiber Reinforcement Polymer material is Carbon Fiber Reinforcement Polymer and Glass Fiber Reinforcement Polymer. These materials is tested when it is installed on concrete cylinders, to obtain the comparison of compressive strength CFRP and GFRP. The dimension of concrete is diameter of 15 cm and height of 30 cm. It is amounted to 15 and divided into three groups. The test is performed until it collapsed to obtain maximum load. The results of research using CFRP and GFRP have shown the significant enhancement in compressive strength. CFRP can increase the compressive strength of 26.89%, and GFRP of 14.89%. For the comparison of two materials, CFRP is more strengthening than GFRP regarding increasing compressive strength. The usage of CFRP and GFRP can increase the loading capacity.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning

PubMed Central

Zhu, Lusha; Mathewson, Kyle E.; Hsu, Ming

2012-01-01

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs. PMID:22307594

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

PubMed

Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood.

PubMed

Daee, Pedram; Mirian, Maryam S; Ahmadabadi, Majid Nili

2014-01-01

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.

Evolution of learning and levels of selection: A lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2013-09-20

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Evolution of learning and levels of selection: a lesson from avian parent-offspring communication.

PubMed

Lotem, Arnon; Biran-Yoeli, Inbar

2014-02-01

In recent years, it has become increasingly clear that the evolution of behavior may be better understood as the evolution of the learning mechanisms that produce it, and that such mechanisms should be modeled and tested explicitly. However, this approach, which has recently been applied to animal foraging and decision-making, has rarely been applied to the social and communicative behaviors that are likely to operate in complex social environments and be subject to multi-level selection. Here we use genetic, agent-based evolutionary simulations to explore how learning mechanisms may evolve to adjust the level of nestling begging (offspring signaling of need), and to examine the possible consequences of this process for parent-offspring conflict and communication. In doing so, we also provide the first step-by-step dynamic model of parent-offspring communication. The results confirm several previous theoretical predictions and demonstrate three novel phenomena. First, negatively frequency-dependent group-level selection can generate a stable polymorphism of learning strategies and parental responses. Second, while conventional reinforcement learning models fail to cope successfully with family dynamics at the nest, a newly developed learning model (incorporating behaviors that are consistent with recent experimental results on learning in nestling begging) produced effective learning, which evolved successfully. Third, while kin-selection affects the frequency of the different learning genes, its impact on begging slope and intensity was unexpectedly negligible, demonstrating that evolution is a complex process, and showing that the effect of kin-selection on behaviors that are shaped by learning may not be predicted by simple application of Hamilton's rule. Copyright © 2013 Elsevier Inc. All rights reserved.

Wiki technology enhanced group project to promote active learning in a neuroscience course for first-year medical students: an exploratory study.

PubMed

Mi, Misa; Gould, Douglas

2014-01-01

A Wiki group project was integrated into a neuroscience course for first-year medical students. The project was developed as a self-directed, collaborative learning task to help medical students review course content and make clinically important connections. The goals of the project were to enhance students' understanding of key concepts in neuroscience, promote active learning, and reinforce their information literacy skills. The objective of the exploratory study was to provide a formative evaluation of the Wiki group project and to examine how Wiki technology was utilized to enhance active and collaborative learning of first-year medical students in the course and to reinforce information literacy skills.

Stuttering Thoughts: Negative Self-Referent Thinking Is Less Sensitive to Aversive Outcomes in People with Higher Levels of Depressive Symptoms

PubMed Central

Iijima, Yudai; Takano, Keisuke; Boddez, Yannick; Raes, Filip; Tanno, Yoshihiko

2017-01-01

Learning theories of depression have proposed that depressive cognitions, such as negative thoughts with reference to oneself, can develop through a reinforcement learning mechanism. This negative self-reference is considered to be positively reinforced by rewarding experiences such as genuine support from others after negative self-disclosure, and negatively reinforced by avoidance of potential aversive situations. The learning account additionally predicts that negative self-reference would be maintained by an inability to adjust one’s behavior when negative self-reference no longer leads to such reward. To test this prediction, we designed an adapted version of the reversal-learning task. In this task, participants were reinforced to choose and engage in either negative or positive self-reference by probabilistic economic reward and punishment. Although participants were initially trained to choose negative self-reference, the stimulus-reward contingencies were reversed to prompt a shift toward positive self-reference (Study 1) and a further shift toward negative self-reference (Study 2). Model-based computational analyses showed that depressive symptoms were associated with a low learning rate of negative self-reference, indicating a high level of reward expectancy for negative self-reference even after the contingency reversal. Furthermore, the difficulty in updating outcome predictions of negative self-reference was significantly associated with the extent to which one possesses negative self-images. These results suggest that difficulty in adjusting action-outcome estimates for negative self-reference increases the chance to be faced with negative aspects of self, which may result in depressive symptoms. PMID:28824511

Negative mood reverses devaluation of goal-directed drug-seeking favouring an incentive learning account of drug dependence.

PubMed

Hogarth, Lee; He, Zhimin; Chase, Henry W; Wills, Andy J; Troisi, Joseph; Leventhal, Adam M; Mathew, Amanda R; Hitsman, Brian

2015-09-01

Two theories explain how negative mood primes smoking behaviour. The stimulus-response (S-R) account argues that in the negative mood state, smoking is experienced as more reinforcing, establishing a direct (automatic) association between the negative mood state and smoking behaviour. By contrast, the incentive learning account argues that in the negative mood state smoking is expected to be more reinforcing, which integrates with instrumental knowledge of the response required to produce that outcome. One differential prediction is that whereas the incentive learning account anticipates that negative mood induction could augment a novel tobacco-seeking response in an extinction test, the S-R account could not explain this effect because the extinction test prevents S-R learning by omitting experience of the reinforcer. To test this, overnight-deprived daily smokers (n = 44) acquired two instrumental responses for tobacco and chocolate points, respectively, before smoking to satiety. Half then received negative mood induction to raise the expected value of tobacco, opposing satiety, whilst the remainder received positive mood induction. Finally, a choice between tobacco and chocolate was measured in extinction to test whether negative mood could augment tobacco choice, opposing satiety, in the absence of direct experience of tobacco reinforcement. Negative mood induction not only abolished the devaluation of tobacco choice, but participants with a significant increase in negative mood increased their tobacco choice in extinction, despite satiety. These findings suggest that negative mood augments drug-seeking by raising the expected value of the drug through incentive learning, rather than through automatic S-R control.

Identifying Cognitive Remediation Change Through Computational Modelling—Effects on Reinforcement Learning in Schizophrenia

PubMed Central

Cella, Matteo; Bishara, Anthony J.; Medin, Evelina; Swan, Sarah; Reeder, Clare; Wykes, Til

2014-01-01

Objective: Converging research suggests that individuals with schizophrenia show a marked impairment in reinforcement learning, particularly in tasks requiring flexibility and adaptation. The problem has been associated with dopamine reward systems. This study explores, for the first time, the characteristics of this impairment and how it is affected by a behavioral intervention—cognitive remediation. Method: Using computational modelling, 3 reinforcement learning parameters based on the Wisconsin Card Sorting Test (WCST) trial-by-trial performance were estimated: R (reward sensitivity), P (punishment sensitivity), and D (choice consistency). In Study 1 the parameters were compared between a group of individuals with schizophrenia (n = 100) and a healthy control group (n = 50). In Study 2 the effect of cognitive remediation therapy (CRT) on these parameters was assessed in 2 groups of individuals with schizophrenia, one receiving CRT (n = 37) and the other receiving treatment as usual (TAU, n = 34). Results: In Study 1 individuals with schizophrenia showed impairment in the R and P parameters compared with healthy controls. Study 2 demonstrated that sensitivity to negative feedback (P) and reward (R) improved in the CRT group after therapy compared with the TAU group. R and P parameter change correlated with WCST outputs. Improvements in R and P after CRT were associated with working memory gains and reduction of negative symptoms, respectively. Conclusion: Schizophrenia reinforcement learning difficulties negatively influence performance in shift learning tasks. CRT can improve sensitivity to reward and punishment. Identifying parameters that show change may be useful in experimental medicine studies to identify cognitive domains susceptible to improvement. PMID:24214932

'Proactive' use of cue-context congruence for building reinforcement learning's reward function.

PubMed

Zsuga, Judit; Biro, Klara; Tajti, Gabor; Szilasi, Magdolna Emma; Papp, Csaba; Juhasz, Bela; Gesztelyi, Rudolf

2016-10-28

Reinforcement learning is a fundamental form of learning that may be formalized using the Bellman equation. Accordingly an agent determines the state value as the sum of immediate reward and of the discounted value of future states. Thus the value of state is determined by agent related attributes (action set, policy, discount factor) and the agent's knowledge of the environment embodied by the reward function and hidden environmental factors given by the transition probability. The central objective of reinforcement learning is to solve these two functions outside the agent's control either using, or not using a model. In the present paper, using the proactive model of reinforcement learning we offer insight on how the brain creates simplified representations of the environment, and how these representations are organized to support the identification of relevant stimuli and action. Furthermore, we identify neurobiological correlates of our model by suggesting that the reward and policy functions, attributes of the Bellman equitation, are built by the orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC), respectively. Based on this we propose that the OFC assesses cue-context congruence to activate the most context frame. Furthermore given the bidirectional neuroanatomical link between the OFC and model-free structures, we suggest that model-based input is incorporated into the reward prediction error (RPE) signal, and conversely RPE signal may be used to update the reward-related information of context frames and the policy underlying action selection in the OFC and ACC, respectively. Furthermore clinical implications for cognitive behavioral interventions are discussed.

Stuttering Thoughts: Negative Self-Referent Thinking Is Less Sensitive to Aversive Outcomes in People with Higher Levels of Depressive Symptoms.

PubMed

Iijima, Yudai; Takano, Keisuke; Boddez, Yannick; Raes, Filip; Tanno, Yoshihiko

2017-01-01

Learning theories of depression have proposed that depressive cognitions, such as negative thoughts with reference to oneself, can develop through a reinforcement learning mechanism. This negative self-reference is considered to be positively reinforced by rewarding experiences such as genuine support from others after negative self-disclosure, and negatively reinforced by avoidance of potential aversive situations. The learning account additionally predicts that negative self-reference would be maintained by an inability to adjust one's behavior when negative self-reference no longer leads to such reward. To test this prediction, we designed an adapted version of the reversal-learning task. In this task, participants were reinforced to choose and engage in either negative or positive self-reference by probabilistic economic reward and punishment. Although participants were initially trained to choose negative self-reference, the stimulus-reward contingencies were reversed to prompt a shift toward positive self-reference (Study 1) and a further shift toward negative self-reference (Study 2). Model-based computational analyses showed that depressive symptoms were associated with a low learning rate of negative self-reference, indicating a high level of reward expectancy for negative self-reference even after the contingency reversal. Furthermore, the difficulty in updating outcome predictions of negative self-reference was significantly associated with the extent to which one possesses negative self-images. These results suggest that difficulty in adjusting action-outcome estimates for negative self-reference increases the chance to be faced with negative aspects of self, which may result in depressive symptoms.

Posterior parietal cortex is critical for the encoding, consolidation, and retrieval of a memory that guides attention for learning.

PubMed

Schiffino, Felipe L; Zhou, Vivian; Holland, Peter C

2014-02-01

Within most contemporary learning theories, reinforcement prediction error, the difference between the obtained and expected reinforcer value, critically influences associative learning. In some theories, this prediction error determines the momentary effectiveness of the reinforcer itself, such that the same physical event produces more learning when its presentation is surprising than when it is expected. In other theories, prediction error enhances attention to potential cues for that reinforcer by adjusting cue-specific associability parameters, biasing the processing of those stimuli so that they more readily enter into new associations in the future. A unique feature of these latter theories is that such alterations in stimulus associability must be represented in memory in an enduring fashion. Indeed, considerable data indicate that altered associability may be expressed days after its induction. Previous research from our laboratory identified brain circuit elements critical to the enhancement of stimulus associability by the omission of an expected event, and to the subsequent expression of that altered associability in more rapid learning. Here, for the first time, we identified a brain region, the posterior parietal cortex, as a potential site for a memorial representation of altered stimulus associability. In three experiments using rats and a serial prediction task, we found that intact posterior parietal cortex function was essential during the encoding, consolidation, and retrieval of an associability memory enhanced by surprising omissions. We discuss these new results in the context of our previous findings and additional plausible frontoparietal and subcortical networks. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Multiagent cooperation and competition with deep reinforcement learning.

PubMed

Tampuu, Ardi; Matiisen, Tambet; Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments.

Emotional Multiagent Reinforcement Learning in Spatial Social Dilemmas.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui; Tan, Guozhen

2015-12-01

Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

A self-learning rule base for command following in dynamical systems

NASA Technical Reports Server (NTRS)

Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

Multiagent cooperation and competition with deep reinforcement learning

PubMed Central

Kodelja, Dorian; Kuzovkin, Ilya; Korjus, Kristjan; Aru, Juhan; Aru, Jaan; Vicente, Raul

2017-01-01

Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by reinforcement while using only their raw visual input as the state representation. In particular, we extend the Deep Q-Learning framework to multiagent environments to investigate the interaction between two learning agents in the well-known video game Pong. By manipulating the classical rewarding scheme of Pong we show how competitive and collaborative behaviors emerge. We also describe the progression from competitive to collaborative behavior when the incentive to cooperate is increased. Finally we show how learning by playing against another adaptive agent, instead of against a hard-wired algorithm, results in more robust strategies. The present work shows that Deep Q-Networks can become a useful tool for studying decentralized learning of multiagent systems coping with high-dimensional environments. PMID:28380078

Constructing Temporally Extended Actions through Incremental Community Detection

PubMed Central

Li, Ge

2018-01-01

Hierarchical reinforcement learning works on temporally extended actions or skills to facilitate learning. How to automatically form such abstraction is challenging, and many efforts tackle this issue in the options framework. While various approaches exist to construct options from different perspectives, few of them concentrate on options' adaptability during learning. This paper presents an algorithm to create options and enhance their quality online. Both aspects operate on detected communities of the learning environment's state transition graph. We first construct options from initial samples as the basis of online learning. Then a rule-based community revision algorithm is proposed to update graph partitions, based on which existing options can be continuously tuned. Experimental results in two problems indicate that options from initial samples may perform poorly in more complex environments, and our presented strategy can effectively improve options and get better results compared with flat reinforcement learning. PMID:29849543

Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.

PubMed

Deng, Yue; Bao, Feng; Kong, Youyong; Ren, Zhiquan; Dai, Qionghai

2017-03-01

Can we train the computer to beat experienced traders for financial assert trading? In this paper, we try to address this challenge by introducing a recurrent deep neural network (NN) for real-time financial signal representation and trading. Our model is inspired by two biological-related learning concepts of deep learning (DL) and reinforcement learning (RL). In the framework, the DL part automatically senses the dynamic market condition for informative feature learning. Then, the RL module interacts with deep representations and makes trading decisions to accumulate the ultimate rewards in an unknown environment. The learning system is implemented in a complex NN that exhibits both the deep and recurrent structures. Hence, we propose a task-aware backpropagation through time method to cope with the gradient vanishing issue in deep training. The robustness of the neural system is verified on both the stock and the commodity future markets under broad testing conditions.

"Learned Helplessness" or "Learned Incompetence"?

ERIC Educational Resources Information Center

Sergent, Justine; Lambert, Wallace E.

Studies in the past have shown that reinforcements independent of the subjects actions may induce a feeling of helplessness. Most experiments on learned helplessness have led researchers to believe that uncontrollability (non-contingency of feedback upon response) was the determining feature of learned helplessness, although in most studies…

Implicit learning in cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia).

PubMed

Locurto, Charles; Fox, Maura; Mazzella, Andrea

2015-06-01

There is considerable interest in the conditions under which human subjects learn patterned information without explicit instructions to learn that information. This form of learning, termed implicit or incidental learning, can be approximated in nonhumans by exposing subjects to patterned information but delivering reinforcement randomly, thereby not requiring the subjects to learn the information in order to be reinforced. Following acquisition, nonhuman subjects are queried as to what they have learned about the patterned information. In the present experiment, we extended the study of implicit learning in nonhumans by comparing two species, cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia), on an implicit learning task that used an artificial grammar to generate the patterned elements for training. We equated the conditions of training and testing as much as possible between the two species. The results indicated that both species demonstrated approximately the same magnitude of implicit learning, judged both by a random test and by choice tests between pairs of training elements. This finding suggests that the ability to extract patterned information from situations in which such learning is not demanded is of longstanding origin.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Social learning through prediction error in the brain

NASA Astrophysics Data System (ADS)

Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

2017-06-01

Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma

PubMed Central

Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E.; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also. PMID:29228001

Synchronization of Chaotic Systems without Direct Connections Using Reinforcement Learning

NASA Astrophysics Data System (ADS)

Sato, Norihisa; Adachi, Masaharu

In this paper, we propose a control method for the synchronization of chaotic systems that does not require the systems to be connected, unlike existing methods such as that proposed by Pecora and Carroll in 1990. The method is based on the reinforcement learning algorithm. We apply our method to two discrete-time chaotic systems with mismatched parameters and achieve M step delay synchronization. Moreover, we extend the proposed method to the synchronization of continuous-time chaotic systems.

Effects of Ventral Striatum Lesions on Stimulus-Based versus Action-Based Reinforcement Learning.

PubMed

Rothenhoefer, Kathryn M; Costa, Vincent D; Bartolo, Ramón; Vicario-Feliciano, Raquel; Murray, Elisabeth A; Averbeck, Bruno B

2017-07-19

Learning the values of actions versus stimuli may depend on separable neural circuits. In the current study, we evaluated the performance of rhesus macaques with ventral striatum (VS) lesions on a two-arm bandit task that had randomly interleaved blocks of stimulus-based and action-based reinforcement learning (RL). Compared with controls, monkeys with VS lesions had deficits in learning to select rewarding images but not rewarding actions. We used a RL model to quantify learning and choice consistency and found that, in stimulus-based RL, the VS lesion monkeys were more influenced by negative feedback and had lower choice consistency than controls. Using a Bayesian model to parse the groups' learning strategies, we also found that VS lesion monkeys defaulted to an action-based choice strategy. Therefore, the VS is involved specifically in learning the value of stimuli, not actions. SIGNIFICANCE STATEMENT Reinforcement learning models of the ventral striatum (VS) often assume that it maintains an estimate of state value. This suggests that it plays a general role in learning whether rewards are assigned based on a chosen action or stimulus. In the present experiment, we examined the effects of VS lesions on monkeys' ability to learn that choosing a particular action or stimulus was more likely to lead to reward. We found that VS lesions caused a specific deficit in the monkeys' ability to discriminate between images with different values, whereas their ability to discriminate between actions with different values remained intact. Our results therefore suggest that the VS plays a specific role in learning to select rewarded stimuli. Copyright © 2017 the authors 0270-6474/17/376902-13$15.00/0.

A state of the art review on reinforced concrete beams with openings retrofitted with FRP

NASA Astrophysics Data System (ADS)

Osman, Bashir H.; Wu, Erjun; Ji, Bohai; S Abdelgader, Abdeldime M.

2016-09-01

The use of externally bonded fiber reinforced polymer (FRP) sheets, strips or steel plates is a modern and convenient way for strengthening of reinforced concrete (RC) beams. Several researches have been carried out on reinforced concrete beams with web openings that strengthened using fiber reinforced polymer composite. Majority of researches focused on shear strengthening compared with flexural strengthening, while others studied the effect of openings on shear and flexural separately with various loading. This paper investigates the impact of more than sixty articles on opening reinforced concrete beams with and without strengthening by fiber reinforcement polymers FRP. Moreover, important practical issues, which are contributed in shear strengthening of beams with different strengthening techniques, such as steel plate and FRP laminate, and detailed with various design approaches are discussed. Furthermore, a simple technique of applying fiber reinforced polymer contributed with steel plate for strengthening the RC beams with openings under different load application is concluded. Directions for future research based on the existing gaps of the present works are presented.

Intrinsically motivated reinforcement learning for human-robot interaction in the real-world.

PubMed

Qureshi, Ahmed Hussain; Nakamura, Yutaka; Yoshikawa, Yuichiro; Ishiguro, Hiroshi

2018-03-26

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement. Copyright © 2018 Elsevier Ltd. All rights reserved.

Incorporation of perception-based information in robot learning using fuzzy reinforcement learning agents

NASA Astrophysics Data System (ADS)

Zhou, Changjiu; Meng, Qingchun; Guo, Zhongwen; Qu, Wiefen; Yin, Bo

2002-04-01

Robot learning in unstructured environments has been proved to be an extremely challenging problem, mainly because of many uncertainties always present in the real world. Human beings, on the other hand, seem to cope very well with uncertain and unpredictable environments, often relying on perception-based information. Furthermore, humans beings can also utilize perceptions to guide their learning on those parts of the perception-action space that are actually relevant to the task. Therefore, we conduct a research aimed at improving robot learning through the incorporation of both perception-based and measurement-based information. For this reason, a fuzzy reinforcement learning (FRL) agent is proposed in this paper. Based on a neural-fuzzy architecture, different kinds of information can be incorporated into the FRL agent to initialise its action network, critic network and evaluation feedback module so as to accelerate its learning. By making use of the global optimisation capability of GAs (genetic algorithms), a GA-based FRL (GAFRL) agent is presented to solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can perform a more effective global search. Different GAFRL agents are constructed and verified by using the simulation model of a physical biped robot. The simulation analysis shows that the biped learning rate for dynamic balance can be improved by incorporating perception-based information on biped balancing and walking evaluation. The biped robot can find its application in ocean exploration, detection or sea rescue activity, as well as military maritime activity.

A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention—A Neuroeducation Study

PubMed Central

Anderson, Sarah J.; Hecker, Kent G.; Krigolson, Olave E.; Jamniczky, Heather A.

2018-01-01

In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise. PMID:29467638

A Reinforcement-Based Learning Paradigm Increases Anatomical Learning and Retention-A Neuroeducation Study.

PubMed

Anderson, Sarah J; Hecker, Kent G; Krigolson, Olave E; Jamniczky, Heather A

2018-01-01

In anatomy education, a key hurdle to engaging in higher-level discussion in the classroom is recognizing and understanding the extensive terminology used to identify and describe anatomical structures. Given the time-limited classroom environment, seeking methods to impart this foundational knowledge to students in an efficient manner is essential. Just-in-Time Teaching (JiTT) methods incorporate pre-class exercises (typically online) meant to establish foundational knowledge in novice learners so subsequent instructor-led sessions can focus on deeper, more complex concepts. Determining how best do we design and assess pre-class exercises requires a detailed examination of learning and retention in an applied educational context. Here we used electroencephalography (EEG) as a quantitative dependent variable to track learning and examine the efficacy of JiTT activities to teach anatomy. Specifically, we examined changes in the amplitude of the N250 and reward positivity event-related brain potential (ERP) components alongside behavioral performance as novice students participated in a series of computerized reinforcement-based learning modules to teach neuroanatomical structures. We found that as students learned to identify anatomical structures, the amplitude of the N250 increased and reward positivity amplitude decreased in response to positive feedback. Both on a retention and transfer exercise when learners successfully remembered and translated their knowledge to novel images, the amplitude of the reward positivity remained decreased compared to early learning. Our findings suggest ERPs can be used as a tool to track learning, retention, and transfer of knowledge and that employing the reinforcement learning paradigm is an effective educational approach for developing anatomical expertise.

Positive Reinforcement in the Vocational Training of Trainable Mental Retardate: A Case Report. Working Paper No. 33.

ERIC Educational Resources Information Center

Peterson, Craig M.

A system of task analysis and positive reinforcement was used in the vocational training of a 19-year-old trainable retarded youth (MA=6 years). The task of polishing shoe skates was analyzed and programmed into 29 steps and was reinforced with praise and money. The trainee learned the task in 13 sessions (approximately 1 month) and was employed…

Consumption-leisure tradeoffs in pigeons: Effects of changing marginal wage rates by varying amount of reinforcement.

PubMed

Green, L; Kagel, J H; Battalio, R C

1987-01-01

Pigeons' rates of responding and food reinforcement under simple random-ratio schedules were compared with those obtained under comparable ratio schedules in which free food deliveries were added, but the duration of each food delivery was halved. These ratio-with-free-food schedules were constructed so that, were the pigeon to maintain the same rate of responding as it had under the simple ratio schedule, total food obtained (earned plus free) would remain unchanged. However, any reduction in responding would reduce total food consumption below that under the simple ratio schedule. These "compensated wage decreases" led to decreases in responding and decreases in food consumption, as predicted by an economic model of labor supply. Moreover, the reductions in responding increased as the ratio value increased (i.e., as wage rates decreased). Pigeons, therefore, substituted leisure for consumption. The relationship between these procedures and negative-income-tax programs is noted.

TRAINING IN INDUSTRY--THE MANAGEMENT OF LEARNING.

ERIC Educational Resources Information Center

BASS, BERNARD M.; VAUGHAN, JAMES A.

THE PRINCIPLES OF LEARNING BEHAVIOR DERIVED THROUGH LABORATORY STUDY CAN BE EXTENDED TO EXPLAIN MUCH OF THE COMPLEX LEARNING REQUIRED IN INDUSTRIAL TRAINING PROGRAMS. A REVIEW OF THE BASIC PRINCIPLES OF HUMAN LEARNING INTRODUCES FOUR BASIC CONCEPTS--DRIVE, STIMULUS, RESPONSE, AND REINFORCER--AND DISCUSSES CLASSICAL AND INSTRUMENTAL CONDITIONING…

What Does a Cultural Approach Offer to Research on Learning?

ERIC Educational Resources Information Center

Hatano, Giyoo; Miyake, Naomi

1991-01-01

The articles of this issue argue that taking culture into consideration in research on learning can enhance understanding of a variety of phenomena related to learning. Discussion reinforces this position and further considers learning processes that are or are not determined by culture. (SLD)

Learning Theory and Prosocial Behavior

ERIC Educational Resources Information Center

Rosenhan, D. L.

1972-01-01

Although theories of learning which stress the role of reinforcement can help us understand altruistic behaviors, it seems clear that a more complete comprehension calls for an expansion of our notions of learning, such that they incorporate affect and cognition. (Author/JM)

Local Service Learning in Teacher Preparation Program

ERIC Educational Resources Information Center

Nuangchalerm, Prasart

2016-01-01

The local knowledge is simply integrated in education and learning process. This study aims to promote local knowledge in school through service learning. The learning process is employed herbal plants to reinforce students learn how to sustain local knowledge with modern life and 21st century classroom. Participants consisted of 42 pre-service…

Computer Assisted Language Learning. Routledge Studies in Computer Assisted Language Learning

ERIC Educational Resources Information Center

Pennington, Martha

2011-01-01

Computer-assisted language learning (CALL) is an approach to language teaching and learning in which computer technology is used as an aid to the presentation, reinforcement and assessment of material to be learned, usually including a substantial interactive element. This books provides an up-to date and comprehensive overview of…

Hippocampus NMDA receptors selectively mediate latent extinction of place learning.

PubMed

Goodman, Jarid; Gabriele, Amanda; Packard, Mark G

2016-09-01

Extinction of maze learning may be achieved with or without the animal performing the previously acquired response. In typical "response extinction," animals are given the opportunity to make the previously acquired approach response toward the goal location of the maze without reinforcement. In "latent extinction," animals are not given the opportunity to make the previously acquired response and instead are confined to the previous goal location without reinforcement. Previous evidence indicates that the effectiveness of these protocols may depend on the type of memory being extinguished. Thus, one aim of the present study was to further examine the effectiveness of response and latent extinction protocols across dorsolateral striatum (DLS)-dependent response learning and hippocampus-dependent place learning tasks. In addition, previous neural inactivation experiments indicate a selective role for the hippocampus in latent extinction, but have not investigated the precise neurotransmitter mechanisms involved. Thus, the present study also examined whether latent extinction of place learning might depend on NMDA receptor activity in the hippocampus. In experiment 1, adult male Long-Evans rats were trained in a response learning task in a water plus-maze, in which animals were reinforced to make a consistent body-turn response to reach an invisible escape platform. Results indicated that response extinction, but not latent extinction, was effective at extinguishing memory in the response learning task. In experiment 2, rats were trained in a place learning task, in which animals were reinforced to approach a consistent spatial location containing the hidden escape platform. In experiment 2, animals also received intra-hippocampal infusions of the NMDA receptor antagonist 2-amino-5-phosphopentanoic acid (AP5; 5.0 or 7.5 ug/0.5 µg) or saline vehicle immediately before response or latent extinction training. Results indicated that both extinction protocols were effective at extinguishing memory in the place learning task. In addition, intra-hippocampal AP5 (7.5 µg) impaired latent extinction, but not response extinction, suggesting that hippocampal NMDA receptors are selectively involved in latent extinction. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

"Work ethic" in pigeons: reward value is directly related to the effort or time required to obtain the reward.

PubMed

Clement, T S; Feltus, J R; Kaiser, D H; Zentall, T R

2000-03-01

Stimuli associated with less effort or with shorter delays to reinforcement are generally preferred over those associated with greater effort or longer delays to reinforcement. However, the opposite appears to be true of stimuli that follow greater effort or longer delays. In training, a simple simultaneous discrimination followed a single peck to an initial stimulus (S+FR1 S-FR1) and a different simple simultaneous discrimination followed 20 pecks to the initial stimulus (S+FR20 S-FR20). On test trials, pigeons preferred S+FR20 over S+FR1 and S-FR20 over S-FR1. These data support the view that the state of the animal immediately prior to presentation of the discrimination affects the value of the reinforcement that follows it. This contrast effect is analogous to effects that when they occur in humans have been attributed to more complex cognitive and social factors.

The Behavioral Economics of Choice and Interval Timing

PubMed Central

Jozefowiez, J.; Staddon, J. E. R.; Cerutti, D. T.

2009-01-01

We propose a simple behavioral economic model (BEM) describing how reinforcement and interval timing interact. The model assumes a Weber-law-compliant logarithmic representation of time. Associated with each represented time value are the payoffs that have been obtained for each possible response. At a given real time, the response with the highest payoff is emitted. The model accounts for a wide range of data from procedures such as simple bisection, metacognition in animals, economic effects in free-operant psychophysical procedures and paradoxical choice in double-bisection procedures. Although it assumes logarithmic time representation, it can also account for data from the time-left procedure usually cited in support of linear time representation. It encounters some difficulties in complex free-operant choice procedures, such as concurrent mixed fixed-interval schedules as well as some of the data on double bisection, that may involve additional processes. Overall, BEM provides a theoretical framework for understanding how reinforcement and interval timing work together to determine choice between temporally differentiated reinforcers. PMID:19618985

Micromechanics and Structural Response of Functionally Graded, Particulate-Matrix, Fiber-Reinforced Composites

PubMed Central

Genin, Guy M.; Birman, Victor

2009-01-01

Reinforcement of fibrous composites by stiff particles embedded in the matrix offers the potential for simple, economical functional grading, enhanced response to mechanical loads, and improved functioning at high temperatures. Here, we consider laminated plates made of such a material, with spherical reinforcement tailored by layer. The moduli for this material lie within relatively narrow bounds. Two separate moduli estimates are considered: a “two-step” approach in which fibers are embedded in a homogenized particulate matrix, and the Kanaun-Jeulin (2001) approach, which we re-derive in a simple way using the Benveniste (1988) method. Optimal tailoring of a plate is explored, and functional grading is shown to improve the performance of the structures considered. In the example of a square, simply supported, cross-ply laminated panel subjected to uniform transverse pressure, a modest functional grading offers significant improvement in performance. A second example suggests superior blast resistance of the panel achieved at the expense of only a small increase in weight. PMID:23874001

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, Kyoungsoo, E-mail: kpark16@illinois.ed; Paulino, Glaucio H.; Roesler, Jeffery

A simple, effective, and practical constitutive model for cohesive fracture of fiber reinforced concrete is proposed by differentiating the aggregate bridging zone and the fiber bridging zone. The aggregate bridging zone is related to the total fracture energy of plain concrete, while the fiber bridging zone is associated with the difference between the total fracture energy of fiber reinforced concrete and the total fracture energy of plain concrete. The cohesive fracture model is defined by experimental fracture parameters, which are obtained through three-point bending and split tensile tests. As expected, the model describes fracture behavior of plain concrete beams. Inmore » addition, it predicts the fracture behavior of either fiber reinforced concrete beams or a combination of plain and fiber reinforced concrete functionally layered in a single beam specimen. The validated model is also applied to investigate continuously, functionally graded fiber reinforced concrete composites.« less

FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks.

PubMed

Zhang, Zhen; Zhao, Dongbin; Gao, Junwei; Wang, Dongqing; Dai, Yujie

2017-06-01

In this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.

Fault-tolerant optimised tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology

NASA Astrophysics Data System (ADS)

Han, Ke-Zhen; Feng, Jian; Cui, Xiaohong

2017-10-01

This paper considers the fault-tolerant optimised tracking control (FTOTC) problem for unknown discrete-time linear system. A research scheme is proposed on the basis of data-based parity space identification, reinforcement learning and residual compensation techniques. The main characteristic of this research scheme lies in the parity-space-identification-based simultaneous tracking control and residual compensation. The specific technical line consists of four main contents: apply subspace aided method to design observer-based residual generator; use reinforcement Q-learning approach to solve optimised tracking control policy; rely on robust H∞ theory to achieve noise attenuation; adopt fault estimation triggered by residual generator to perform fault compensation. To clarify the design and implementation procedures, an integrated algorithm is further constructed to link up these four functional units. The detailed analysis and proof are subsequently given to explain the guaranteed FTOTC performance of the proposed conclusions. Finally, a case simulation is provided to verify its effectiveness.

Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment.

PubMed

Zhang, Yunfeng; Paik, Jaehyon; Pirolli, Peter

2015-04-01

Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm. Copyright © 2015 Cognitive Science Society, Inc.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Learning Sequences of Actions in Collectives of Autonomous Agents

NASA Technical Reports Server (NTRS)

Turner, Kagan; Agogino, Adrian K.; Wolpert, David H.; Clancy, Daniel (Technical Monitor)

2001-01-01

In this paper we focus on the problem of designing a collective of autonomous agents that individually learn sequences of actions such that the resultant sequence of joint actions achieves a predetermined global objective. We are particularly interested in instances of this problem where centralized control is either impossible or impractical. For single agent systems in similar domains, machine learning methods (e.g., reinforcement learners) have been successfully used. However, applying such solutions directly to multi-agent systems often proves problematic, as agents may work at cross-purposes, or have difficulty in evaluating their contribution to achievement of the global objective, or both. Accordingly, the crucial design step in multiagent systems centers on determining the private objectives of each agent so that as the agents strive for those objectives, the system reaches a good global solution. In this work we consider a version of this problem involving multiple autonomous agents in a grid world. We use concepts from collective intelligence to design goals for the agents that are 'aligned' with the global goal, and are 'learnable' in that agents can readily see how their behavior affects their utility. We show that reinforcement learning agents using those goals outperform both 'natural' extensions of single agent algorithms and global reinforcement, learning solutions based on 'team games'.

Continuous theta-burst stimulation (cTBS) over the lateral prefrontal cortex alters reinforcement learning bias.

PubMed

Ott, Derek V M; Ullsperger, Markus; Jocham, Gerhard; Neumann, Jane; Klein, Tilmann A

2011-07-15

The prefrontal cortex is known to play a key role in higher-order cognitive functions. Recently, we showed that this brain region is active in reinforcement learning, during which subjects constantly have to integrate trial outcomes in order to optimize performance. To further elucidate the role of the dorsolateral prefrontal cortex (DLPFC) in reinforcement learning, we applied continuous theta-burst stimulation (cTBS) either to the left or right DLPFC, or to the vertex as a control region, respectively, prior to the performance of a probabilistic learning task in an fMRI environment. While there was no influence of cTBS on learning performance per se, we observed a stimulation-dependent modulation of reward vs. punishment sensitivity: Left-hemispherical DLPFC stimulation led to a more reward-guided performance, while right-hemispherical cTBS induced a more avoidance-guided behavior. FMRI results showed enhanced prediction error coding in the ventral striatum in subjects stimulated over the left as compared to the right DLPFC. Both behavioral and imaging results are in line with recent findings that left, but not right-hemispherical stimulation can trigger a release of dopamine in the ventral striatum, which has been suggested to increase the relative impact of rewards rather than punishment on behavior. Copyright © 2011 Elsevier Inc. All rights reserved.

Associability-modulated loss learning is increased in posttraumatic stress disorder

PubMed Central

Brown, Vanessa M; Zhu, Lusha; Wang, John M; Frueh, B Christopher

2018-01-01

Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat-deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention-based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets. PMID:29313489

Rules and mechanisms for efficient two-stage learning in neural circuits.

PubMed

Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

2017-04-04

Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

Ethanol Seeking by Long Evans Rats Is Not Always a Goal-Directed Behavior

PubMed Central

Mangieri, Regina A.; Cofresí, Roberto U.; Gonzales, Rueben A.

2012-01-01

Background Two parallel and interacting processes are said to underlie animal behavior, whereby learning and performance of a behavior is at first via conscious and deliberate (goal-directed) processes, but after initial acquisition, the behavior can become automatic and stimulus-elicited (habitual). With respect to instrumental behaviors, animal learning studies suggest that the duration of training and the action-outcome contingency are two factors involved in the emergence of habitual seeking of “natural” reinforcers (e.g., sweet solutions, food or sucrose pellets). To rigorously test whether behaviors reinforced by abused substances such as ethanol, in particular, similarly become habitual was the primary aim of this study. Methodology/Principal Findings Male Long Evans rats underwent extended or limited operant lever press training with 10% sucrose/10% ethanol (10S10E) reinforcement (variable interval (VI) or (VR) ratio schedule of reinforcement), or with 10% sucrose (10S) reinforcement (VI schedule only). Once training and pretesting were complete, the impact of outcome devaluation on operant behavior was evaluated after lithium chloride injections were paired with the reinforcer, or unpaired 24 hours later. After limited, but not extended instrumental training, lever pressing by groups trained under VR with 10S10E and under VI with 10S was sensitive to outcome devaluation. In contrast, responding by both the extended and limited training 10S10E VI groups was not sensitive to ethanol devaluation during the test for habitual behavior. Conclusions/Significance Operant behavior by rats trained to self-administer an ethanol-sucrose solution showed variable sensitivity to a change in the value of ethanol, with relative insensitivity developing sooner in animals that received time-variable ethanol reinforcement during training sessions. One important implication, with respect to substance abuse in humans, is that initial learning about the relationship between instrumental actions and the opportunity to consume ethanol-containing drinks can influence the time course for the development or expression of habitual ethanol seeking behavior. PMID:22870342

Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia.

PubMed

Collins, Anne G E; Albrecht, Matthew A; Waltz, James A; Gold, James M; Frank, Michael J

2017-09-15

When studying learning, researchers directly observe only the participants' choices, which are often assumed to arise from a unitary learning process. However, a number of separable systems, such as working memory (WM) and reinforcement learning (RL), contribute simultaneously to human learning. Identifying each system's contributions is essential for mapping the neural substrates contributing in parallel to behavior; computational modeling can help to design tasks that allow such a separable identification of processes and infer their contributions in individuals. We present a new experimental protocol that separately identifies the contributions of RL and WM to learning, is sensitive to parametric variations in both, and allows us to investigate whether the processes interact. In experiments 1 and 2, we tested this protocol with healthy young adults (n = 29 and n = 52, respectively). In experiment 3, we used it to investigate learning deficits in medicated individuals with schizophrenia (n = 49 patients, n = 32 control subjects). Experiments 1 and 2 established WM and RL contributions to learning, as evidenced by parametric modulations of choice by load and delay and reward history, respectively. They also showed interactions between WM and RL, where RL was enhanced under high WM load. Moreover, we observed a cost of mental effort when controlling for reinforcement history: participants preferred stimuli they encountered under low WM load. Experiment 3 revealed selective deficits in WM contributions and preserved RL value learning in individuals with schizophrenia compared with control subjects. Computational approaches allow us to disentangle contributions of multiple systems to learning and, consequently, to further our understanding of psychiatric diseases. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Toward a Science of Learning Games

ERIC Educational Resources Information Center

Howard-Jones, Paul; Demetriou, Skevi; Bogacz, Rafal; Yoo, Jee H.; Leonards, Ute

2011-01-01

Reinforcement learning involves a tight coupling of reward-associated behavior and a type of learning that is very different from that promoted by education. However, the emerging understanding of its underlying processes may help derive principles for effective learning games that have, until now, been elusive. This article first reviews findings…

Beyond the Art Lesson: Free-Choice Learning Centers

ERIC Educational Resources Information Center

Werth, Laurie

2010-01-01

In this article, the author emphasizes that by providing learning centers in the art studio environment and by providing "free-choice time," art educators can encourage and reinforce the natural learning styles of students. Learning centers give elementary students the freedom to pursue individual artistic expression. They give students an…

Control of a simulated arm using a novel combination of Cerebellar learning mechanisms

NASA Technical Reports Server (NTRS)

Assad, C.; Hartmann, M.; Paulin, M. G.

2001-01-01

We present a model of cerebellar cortex that combines two types of learning: feedforward predicitve association based on local Hebbian-type learning between granule cell ascending branch and parallel fiber inputs, and reinforcement learning with feedback error correction based on climbing fiber activity.

Improving Collaborative Learning by Supporting Casual Encounters in Distance Learning.

ERIC Educational Resources Information Center

Contreras, Juan; Llamas, Rafael; Vizcaino, Aurora; Vavela, Jesus

Casual encounters in a learning environment are very useful in reinforcing previous knowledge and acquiring new knowledge. Such encounters are very common in traditional learning environments and can be used successfully in social environments in which students can discover and construct knowledge through a process of dialogue, negotiation, or…

Reinforcement Learning Models and Their Neural Correlates: An Activation Likelihood Estimation Meta-Analysis

PubMed Central

Kumar, Poornima; Eickhoff, Simon B.; Dombrovski, Alexandre Y.

2015-01-01

Reinforcement learning describes motivated behavior in terms of two abstract signals. The representation of discrepancies between expected and actual rewards/punishments – prediction error – is thought to update the expected value of actions and predictive stimuli. Electrophysiological and lesion studies suggest that mesostriatal prediction error signals control behavior through synaptic modification of cortico-striato-thalamic networks. Signals in the ventromedial prefrontal and orbitofrontal cortex are implicated in representing expected value. To obtain unbiased maps of these representations in the human brain, we performed a meta-analysis of functional magnetic resonance imaging studies that employed algorithmic reinforcement learning models, across a variety of experimental paradigms. We found that the ventral striatum (medial and lateral) and midbrain/thalamus represented reward prediction errors, consistent with animal studies. Prediction error signals were also seen in the frontal operculum/insula, particularly for social rewards. In Pavlovian studies, striatal prediction error signals extended into the amygdala, while instrumental tasks engaged the caudate. Prediction error maps were sensitive to the model-fitting procedure (fixed or individually-estimated) and to the extent of spatial smoothing. A correlate of expected value was found in a posterior region of the ventromedial prefrontal cortex, caudal and medial to the orbitofrontal regions identified in animal studies. These findings highlight a reproducible motif of reinforcement learning in the cortico-striatal loops and identify methodological dimensions that may influence the reproducibility of activation patterns across studies. PMID:25665667

Seizure Control in a Computational Model Using a Reinforcement Learning Stimulation Paradigm.

PubMed

Nagaraj, Vivek; Lamperski, Andrew; Netoff, Theoden I

2017-11-01

Neuromodulation technologies such as vagus nerve stimulation and deep brain stimulation, have shown some efficacy in controlling seizures in medically intractable patients. However, inherent patient-to-patient variability of seizure disorders leads to a wide range of therapeutic efficacy. A patient specific approach to determining stimulation parameters may lead to increased therapeutic efficacy while minimizing stimulation energy and side effects. This paper presents a reinforcement learning algorithm that optimizes stimulation frequency for controlling seizures with minimum stimulation energy. We apply our method to a computational model called the epileptor. The epileptor model simulates inter-ictal and ictal local field potential data. In order to apply reinforcement learning to the Epileptor, we introduce a specialized reward function and state-space discretization. With the reward function and discretization fixed, we test the effectiveness of the temporal difference reinforcement learning algorithm (TD(0)). For periodic pulsatile stimulation, we derive a relation that describes, for any stimulation frequency, the minimal pulse amplitude required to suppress seizures. The TD(0) algorithm is able to identify parameters that control seizures quickly. Additionally, our results show that the TD(0) algorithm refines the stimulation frequency to minimize stimulation energy thereby converging to optimal parameters reliably. An advantage of the TD(0) algorithm is that it is adaptive so that the parameters necessary to control the seizures can change over time. We show that the algorithm can converge on the optimal solution in simulation with slow and fast inter-seizure intervals.

Developing skills versus reinforcing concepts in physics labs: Insight from a survey of students' beliefs about experimental physics

NASA Astrophysics Data System (ADS)

Wilcox, Bethany R.; Lewandowski, H. J.

2017-06-01

Physics laboratory courses have been generally acknowledged as an important component of the undergraduate curriculum, particularly with respect to developing students' interest in, and understanding of, experimental physics. There are a number of possible learning goals for these courses including reinforcing physics concepts, developing laboratory skills, and promoting expertlike beliefs about the nature of experimental physics. However, there is little consensus among instructors and researchers interested in the laboratory learning environment as to the relative importance of these various learning goals. Here, we contribute data to this debate through the analysis of students' responses to the laboratory-focused assessment known as the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS). Using a large, national data set of students' responses, we compare students' E-CLASS performance in classes in which the instructor self-reported focusing on developing skills, reinforcing concepts, or both. As the classification of courses was based on instructor self-report, we also provide additional description of these courses with respect to how often students engage in particular activities in the lab. We find that courses that focus specifically on developing lab skills have more expertlike postinstruction E-CLASS responses than courses that focus either on reinforcing physics concepts or on both goals. Within first-year courses, this effect is larger for women. Moreover, these findings hold when controlling for the variance in postinstruction scores that is associated with preinstruction E-CLASS scores, student major, and student gender.

Enhancing second-order conditioning with lesions of the basolateral amygdala.

PubMed

Holland, Peter C

2016-04-01

Because the occurrence of primary reinforcers in natural environments is relatively rare, conditioned reinforcement plays an important role in many accounts of behavior, including pathological behaviors such as the abuse of alcohol or drugs. As a result of pairing with natural or drug reinforcers, initially neutral cues acquire the ability to serve as reinforcers for subsequent learning. Accepting a major role for conditioned reinforcement in everyday learning is complicated by the often-evanescent nature of this phenomenon in the laboratory, especially when primary reinforcers are entirely absent from the test situation. Here, I found that under certain conditions, the impact of conditioned reinforcement could be extended by lesions of the basolateral amygdala (BLA). Rats received first-order Pavlovian conditioning pairings of 1 visual conditioned stimulus (CS) with food prior to receiving excitotoxic or sham lesions of the BLA, and first-order pairings of another visual CS with food after that surgery. Finally, each rat received second-order pairings of a different auditory cue with each visual first-order CS. As in prior studies, relative to sham-lesioned control rats, lesioned rats were impaired in their acquisition of second-order conditioning to the auditory cue paired with the first-order CS that was trained after surgery. However, lesioned rats showed enhanced and prolonged second-order conditioning to the auditory cue paired with the first-order CS that was trained before amygdala damage was made. Implications for an enhanced role for conditioned reinforcement by drug-related cues after drug-induced alterations in neural plasticity are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Motivation of extended behaviors by anterior cingulate cortex.

PubMed

Holroyd, Clay B; Yeung, Nick

2012-02-01

Intense research interest over the past decade has yielded diverse and often discrepant theories about the function of anterior cingulate cortex (ACC). In particular, a dichotomy has emerged between neuropsychological theories suggesting a primary role for ACC in motivating or 'energizing' behavior, and neuroimaging-inspired theories emphasizing its contribution to cognitive control and reinforcement learning. To reconcile these views, we propose that ACC supports the selection and maintenance of 'options' - extended, context-specific sequences of behavior directed toward particular goals - that are learned through a process of hierarchical reinforcement learning. This theory accounts for ACC activity in relation to learning and control while simultaneously explaining the effects of ACC damage as disrupting the motivational context supporting the production of goal-directed action sequences. Copyright © 2011 Elsevier Ltd. All rights reserved.