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Reinforcement Learning

An Introduction - 180 - 14629399

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

88592662
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Details
Artikel-Nr.:
88592662
Im Sortiment seit:
12.11.2018
Erscheinungsdatum:
13.11.2018
Medium:
Buch
Einband:
Gebunden
Auflage:
second edition
Autor:
Sutton, Richard S. (University of Alberta)
Barto, Andrew G. (Co-Director Autonomous Learning Laboratory)
Verlag:
MIT Press Ltd
Imprint:
Adaptive Computation and Machine Learning series
Sprache:
Englisch
Rubrik:
Informatik
Seiten:
552
Abbildungen:
64 color illus., 51 b 115 Illustrations, unspecified
Reihe:
Adaptive Computation and Machine Learning series
Gewicht:
1192 gr
Beschreibung
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.
Information zum Autor
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.
Bilder