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Sinopsis
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.
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Acerca de los autores
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.
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
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A Bradford Book, 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
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Bradford Books, 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
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Reinforcement Learning: An Introduction
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MIT Press Ltd, 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Librería: World of Books (was SecondSale), Montgomery, IL, Estados Unidos de America
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Hardback. Condición: Good. 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. Nº de ref. del artículo: CIN0262193981G
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Reinforcement Learning : An Introduction
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Reinforcement Learning : An Introduction
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Reinforcement Learning: An Introduction
Publicado por
MIT Press Ltd, 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Librería: World of Books Inc, Montgomery, IL, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Hardback. Condición: Good. 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. Nº de ref. del artículo: CIN0262193981G
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)
Publicado por
A Bradford Book, 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Librería: HPB-Red, Dallas, TX, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Hardcover. Condición: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority! Nº de ref. del artículo: S_487069468
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
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Bradford Books (edition First Edition), 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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Librería: BooksRun, Philadelphia, PA, Estados Unidos de America
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Hardcover. Condición: Very Good. First Edition. With dust jacket. 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. Nº de ref. del artículo: 0262193981-11-1-29
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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
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MIT Press Ltd, United States, Cambridge, Mass., 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
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