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Adaptive Computation and Machine Learning Ser.: Reinforcement Learning, Second Edition : An Introduction by Richard S. Sutton and Andrew G. Barto (2018, Hardcover)

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About this product

Product Identifiers

PublisherMIT Press

ISBN-100262039249

ISBN-139780262039246

eBay Product ID (ePID)12038265595

Product Key Features

Number of Pages552 Pages

Publication NameReinforcement Learning, Second Edition : an Introduction

LanguageEnglish

Publication Year2018

SubjectProgramming / Algorithms, Intelligence (Ai) & Semantics, Neural Networks

TypeTextbook

Subject AreaComputers

AuthorRichard S. Sutton, Andrew G. Barto

SeriesAdaptive Computation and Machine Learning Ser.

FormatHardcover

Dimensions

Item Height1.5 in

Item Weight41.6 Oz

Item Length11.2 in

Item Width7.3 in

Additional Product Features

Edition Number2

Intended AudienceTrade

LCCN2018-023826

Dewey Edition21

IllustratedYes

Dewey Decimal006.3/1

SynopsisThe 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.

LC Classification NumberQ325.6.R45 2018