Reinforcement Learning (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
552
Utgivningsdatum
2018-11-13
Upplaga
second edition
Förlag
MIT Press
Medarbetare
Bach, Francis (series ed.)
Illustratör/Fotograf
51B115 Illustrations 64 color illus unspecified
Illustrationer
64 color illus., 51 b&w illus.; 115 Illustrations, unspecified
Dimensioner
234 x 185 x 36 mm
Vikt
1180 g
Antal komponenter
1
ISBN
9780262039246
Reinforcement Learning (inbunden)

Reinforcement Learning

An Introduction

Inbunden Engelska, 2018-11-13
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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.
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Övrig information

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.