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The Machine-Learning Approach of Reinforcement Learning

Thomas Boraud

in How the Brain Makes Decisions

Published in print:
2020
Published Online:
November 2020
ISBN:
9780198824367
eISBN:
9780191863202
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198824367.003.0016
Subject:
Neuroscience, Behavioral Neuroscience

This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle ... More


Neural Correlates of Hierarchical Reinforcement Learning

José J. F. Ribas-Fernandes, Yael Niv, and Matthew M. Botvinick

in Neural Basis of Motivational and Cognitive Control

Published in print:
2011
Published Online:
August 2013
ISBN:
9780262016438
eISBN:
9780262298490
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262016438.003.0016
Subject:
Neuroscience, Behavioral Neuroscience

This chapter discusses the relevance of reinforcement learning (RL) to its hierarchical structure. It first reviews the fundamentals of RL, with a focus on temporal-difference learning in ... More


The Decision-Making Engine

Thomas Boraud

in How the Brain Makes Decisions

Published in print:
2020
Published Online:
November 2020
ISBN:
9780198824367
eISBN:
9780191863202
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198824367.003.0017
Subject:
Neuroscience, Behavioral Neuroscience

This chapter presents an upgrade of the neural network by implementing the reward prediction error. It then compares the final product with the actor-critic model and discusses the similarities and ... More


Reinforcement learning

Thomas P. Trappenberg

in Fundamentals of Machine Learning

Published in print:
2019
Published Online:
January 2020
ISBN:
9780198828044
eISBN:
9780191883873
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198828044.003.0010
Subject:
Neuroscience, Behavioral Neuroscience

The discussion here considers a much more common learning condition where an agent, such as a human or a robot, has to learn to make decisions in the environment from simple feedback. Such feedback ... More


Learning in Large Worlds

John M. McNamara and Olof Leimar

in Game Theory in Biology: concepts and frontiers

Published in print:
2020
Published Online:
November 2020
ISBN:
9780198815778
eISBN:
9780191853456
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198815778.003.0005
Subject:
Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics

The chapter introduces reinforcement learning in game-theory models. A distinction is made between small-worlds models with Bayesian updating and large-worlds models that implement specific ... More


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