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: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condizione: Fair. First Edition. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way. Codice articolo 0262193981-7-1
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Da: ZBK Books, Carlstadt, NJ, U.S.A.
Condizione: good. Fast & Free Shipping â" Good condition with a solid cover and clean pages. Shows normal signs of use such as light wear or a few marks highlighting, but overall a well-maintained copy ready to enjoy. Supplemental items like CDs or access codes may not be included. Codice articolo ZWV.0262193981.G
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Da: Half Price Books Inc., Dallas, TX, U.S.A.
hardcover. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority! Codice articolo S_457084918
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Da: 3Brothers Bookstore, Egg harbor township, NJ, U.S.A.
Condizione: good. Books may contain some notes and highlighting. Supplements such as Access Codes, Cd's Dust Jackets, etc. are not guaranteed with used book purchases. Codice articolo EVV.0262193981.G
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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: Anybook.com, Lincoln, Regno Unito
Condizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. 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 4315704
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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: ReviBlio, Barcelona, B, Spagna
Condition: 15 pages with some highlighted text, the rest excellent. The book provides a clear and simple account of the key ideas and algorithms in this area of artificial intelligence, where an agent learns to maximize a cumulative reward by interacting with a complex, uncertain environment. It covers the history of the field's intellectual foundations and proceeds to the core algorithms and concepts, including: The Reinforcement Learning Problem framed in terms of Markov Decision Processes (MDPs). Basic Solution Methods like Dynamic Programming, Monte Carlo methods, and the influential Temporal-Difference (TD) learning (e.g., Q-learning and SARSA). Function Approximation for handling large state spaces, including the use of artificial neural networks. More advanced topics like policy-gradient methods and a discussion of RL's relationships to psychology and neuroscience. Often referred to as the "bible" of the field, it is a foundational text suitable for students, researchers, and practitioners with a basic understanding of probability. Codice articolo ABE-1760107744142
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Da: YESIBOOKSTORE, MIAMI, FL, U.S.A.
hardcover. Condizione: As New. Codice articolo 0262193981-VB
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Da: Buchpark, Trebbin, Germania
Condizione: Gut. Zustand: Gut | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar. Codice articolo 1509267/203
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