Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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Andrew G. Barto is Professor of Computer Science at the University of Massachusetts.
Richard S. Sutton is Senior Research Scientist, Department of Computer Science, University of Massachusetts.
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Da: Better World Books: West, Reno, NV, U.S.A.
Condizione: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Codice articolo GRP61137464
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Da: Better World Books: West, Reno, NV, U.S.A.
Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Codice articolo 9002566-6
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Da: Little Moon Books, San Francisco, CA, U.S.A.
Hardcover. Condizione: Very Good. Condizione sovraccoperta: Very Good. 1st Edition. Hardcover with dust jacket. Interior clean. Light general wear. 322 pages. Codice articolo ABE-1780472090962
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Da: Book Gurus, Tallahassee, FL, U.S.A.
hardcover. Condizione: Fine. Codice articolo 01KX3R98ZAXRS3Q
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Da: Sekkes Consultants, North Dighton, MA, U.S.A.
Hardcover. Condizione: Near fine. Condizione sovraccoperta: Near fine. 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 when interacting with a complex, uncertain environment. InReinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The only necessary mathematical background is familiarity with elementary concepts of probability. Owner Signature on ffep, fine otherwise. 7¼" - 9¼". Book. Codice articolo 278286
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Da: Goodmediandmore, Asheville, NC, U.S.A.
Condizione: Fair. Some marking on text. Ships next business day from NC. Codice articolo S88-BB5-36.95-012226-A-1.4M-033
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Da: Anybook.com, Lincoln, Regno Unito
Condizione: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in fair condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780262193986. Codice articolo 4315703
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Da: GoldBooks, Denver, CO, U.S.A.
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