Reinforcement Learning: An Introduction

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Richard S. Sutton (University of Alberta) Andrew G. Barto (Co-Director Autonomous Learning Laboratory)

Reinforcement Learning

An Introduction

Richard S. Sutton (University of Alberta), Andrew G. Barto (Co-Director Autonomous Learning Laboratory), Francis Bach (INRIA - Willow Project-Team)

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English

MIT Press
13 November 2018

Machine learning

Series: Adaptive Computation and Machine Learning series

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

By: Richard S. Sutton (University of Alberta), Andrew G. Barto (Co-Director Autonomous Learning Laboratory)
Series edited by: Francis Bach (INRIA - Willow Project-Team)
Imprint: MIT Press
Country of Publication: United States
Edition: second edition
Dimensions: Height: 229mm, Width: 178mm, Spine: 38mm
ISBN: 9780262039246
ISBN 10: 0262039249
Series: Adaptive Computation and Machine Learning series
Pages: 552
Publication Date: 13 November 2018
Recommended Age: From 18 years
Audience: College/higher education , Primary
Format: Hardback
Publisher's Status: Active

Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind. Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst.