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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
ASIN번호 0262039249
상품상태 New   
상품구분 Books / Computers & Technology
판매자 Joyce Stancell
판매자위치 미확인
현지 판매 가격
$56.08
상품가격 상세보기
관련상품



상품설명
Review “This book is the bible of reinforcement learning, and the new edition is particularly timely given the burgeoning activity in the field. No one with an interest in the problem of learning to act - student, researcher, practitioner, or curious nonspecialist - should be without it.”―Professor of Computer Science, University of Washington, and author of The Master Algorithm “Generations of reinforcement learning researchers grew up and were inspired by the first edition of Sutton and Barto's book. The second edition is guaranteed to please previous and new readers: while the new edition significantly expands the range of topics covered (new topics covered include artificial neural networks, Monte-Carlo tree search, average reward maximization, and a chapter on classic and new applications), thus increasing breadth, the authors also managed to increase the depth of the presentation by using cleaner notation and disentangling various aspects of this immense topic. At the same time, the new edition retains the simplicity and directness of explanations, thus retaining the great accessibility of the book to readers of all kinds of backgrounds. A fantastic book that I wholeheartedly recommend those interested in using, developing, or understanding reinforcement learning.”―Csaba Szepesvari, Research Scientist at DeepMind and Professor of Computer Science, University of Alberta "I recommend Sutton and Barto's new edition of Reinforcement Learning to anybody who wants to learn about this increasingly important family of machine learning methods. This second edition expands on the popular first edition, covering today's key algorithms and theory, illustrating these concepts using real-world applications that range from learning to control robots, to learning to defeat the human world-champion Go player, and discussing fundamental connections between these computer algorithms and research on human learning from psychology and neuroscience."―Tom Mitchell, Professor of Computer Science, Carnegie-Mellon University “Still the seminal text on reinforcement learning - the increasingly important technique that underlies many of the most advanced AI systems today. Required reading for anyone seriously interested in the science of AI!”―Demis Hassabis, Cofounder and CEO, DeepMind “The second edition of Reinforcement Learning by Sutton and Barto comes at just the right time. The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. It has been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL.”―Yoshua Bengio, Professor of Computer Science and Operations Research, University of Montreal Read more About the Author 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. Read more








상품설명
Review “This book is the bible of reinforcement learning, and the new edition is particularly timely given the burgeoning activity in the field. No one with an interest in the problem of learning to act - student, researcher, practitioner, or curious nonspecialist - should be without it.”―Professor of Computer Science, University of Washington, and author of The Master Algorithm “Generations of reinforcement learning researchers grew up and were inspired by the first edition of Sutton and Barto's book. The second edition is guaranteed to please previous and new readers: while the new edition significantly expands the range of topics covered (new topics covered include artificial neural networks, Monte-Carlo tree search, average reward maximization, and a chapter on classic and new applications), thus increasing breadth, the authors also managed to increase the depth of the presentation by using cleaner notation and disentangling various aspects of this immense topic. At the same time, the new edition retains the simplicity and directness of explanations, thus retaining the great accessibility of the book to readers of all kinds of backgrounds. A fantastic book that I wholeheartedly recommend those interested in using, developing, or understanding reinforcement learning.”―Csaba Szepesvari, Research Scientist at DeepMind and Professor of Computer Science, University of Alberta "I recommend Sutton and Barto's new edition of Reinforcement Learning to anybody who wants to learn about this increasingly important family of machine learning methods. This second edition expands on the popular first edition, covering today's key algorithms and theory, illustrating these concepts using real-world applications that range from learning to control robots, to learning to defeat the human world-champion Go player, and discussing fundamental connections between these computer algorithms and research on human learning from psychology and neuroscience."―Tom Mitchell, Professor of Computer Science, Carnegie-Mellon University “Still the seminal text on reinforcement learning - the increasingly important technique that underlies many of the most advanced AI systems today. Required reading for anyone seriously interested in the science of AI!”―Demis Hassabis, Cofounder and CEO, DeepMind “The second edition of Reinforcement Learning by Sutton and Barto comes at just the right time. The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. It has been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL.”―Yoshua Bengio, Professor of Computer Science and Operations Research, University of Montreal Read more About the Author 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. Read more




2019-04-04 01:14:57