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.
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
Seller: HPB-Red, Dallas, TX, U.S.A.
Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Seller Inventory # S_469105041
Seller: Goodwill of Silicon Valley, SAN JOSE, CA, U.S.A.
Condition: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear. Seller Inventory # GWSVV.0262193981.G
Seller: Better World Books, Mishawaka, IN, U.S.A.
Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. Seller Inventory # GRP14609677
Seller: Sunshine State Books, Lithia, FL, U.S.A.
hardcover. Condition: As New. Hardback--no flaws. Seller Inventory # BT260506088X48
Seller: BooksRun, Philadelphia, PA, U.S.A.
Hardcover. Condition: Very Good. First Edition. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Seller Inventory # 0262193981-8-1
Seller: Sekkes Consultants, North Dighton, MA, U.S.A.
Hardcover. Condition: Near fine. Dust Jacket Condition: 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. Seller Inventory # 278286
Seller: Goodmediandmore, Asheville, NC, U.S.A.
Some marking on text. Ships next business day from NC. Seller Inventory # S88-BB5-36.95-012226-A-1.4M-033
Seller: Anybook.com, Lincoln, United Kingdom
Condition: 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. Seller Inventory # 4315703
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Seller: ReviBlio, Barcelona, B, Spain
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. Seller Inventory # ABE-1760107744142
Seller: Buchpark, Trebbin, Germany
Condition: Gut. Zustand: Gut | Seiten: 344 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar. Seller Inventory # 1509267/203