Neural mechanism for stochastic behaviour during a competitive game
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TLDR
A biologically plausible model of decision making endowed with synaptic plasticity that follows a reward-dependent stochastic Hebbian learning rule is proposed, which constitutes a biophysical implementation of reinforcement learning and generates quasi-random behaviour robustly in spite of intrinsic biases.About:
This article is published in Neural Networks.The article was published on 2006-10-01 and is currently open access. It has received 81 citations till now. The article focuses on the topics: Matching pennies & Reinforcement learning.read more
Citations
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Neuronal Reward and Decision Signals: From Theories to Data
TL;DR: Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts.
Posted Content
Learning to reinforcement learn
Jane X. Wang,Zeb Kurth-Nelson,Dhruva Tirumala,Hubert Soyer,Joel Z. Leibo,Rémi Munos,Charles Blundell,Dharshan Kumaran,Matthew Botvinick +8 more
TL;DR: Deep Meta-Reinforcement Learning (DML) as discussed by the authors is a meta-learning approach for reinforcement learning, where the learned RL algorithm can differ from the original one in arbitrary ways and is configured to exploit structure in the training domain.
Journal ArticleDOI
Decision Making in Recurrent Neuronal Circuits
TL;DR: In this paper, a cortical circuit mechanism of elemental decision computations is proposed, which depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options.
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Animal Intelligence: Experimental Studies
TL;DR: Thorndike as discussed by the authors pointed out that if the new psychology claimed to be a psychology without a soul, the new animal psychology threatened, and still threatens, to become an animal psychology without consciousness.
Journal ArticleDOI
Neural Basis of Reinforcement Learning and Decision Making
TL;DR: This work has revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action in the reinforcement learning framework.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Book
Theory of Games and Economic Behavior
TL;DR: Theory of games and economic behavior as mentioned in this paper is the classic work upon which modern-day game theory is based, and it has been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations.