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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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The product is a textbook titled "Reinforcement Learning, Second Edition: An Introduction" by authors Richard S. Sutton and Andrew G. Barto. Published by MIT Press in 2018, this hardcover book is part of the Adaptive Computation and Machine Learning Series. It covers topics in programming, algorithms, neural networks, and artificial intelligence, specifically focusing on reinforcement learning. With 552 pages, this textbook presents a comprehensive introduction to the subject area, making it a valuable resource for students and professionals in the field of computer science and machine learning.

About this product

Product Identifiers

PublisherMIT Press
ISBN-100262039249
ISBN-139780262039246
eBay Product ID (ePID)12038265595

Product Key Features

Number of Pages552 Pages
LanguageEnglish
Publication NameReinforcement Learning, Second Edition : an Introduction
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 Height1.6 in
Item Weight46.3 Oz
Item Length9.2 in
Item Width7.2 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

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  • by pnich51
    Mar 26, 2025

    best inteo

    it's the to-go book for learning the fundamentals of RL.

    Verified purchase: YesCondition: New

  • by us_rubay
    Nov 12, 2019

    Right on time

    Nicely packaged and arrived right on time.

    Verified purchase: YesCondition: New

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