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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)

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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, MonteCarlo 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 realworld applications that range from learning to control robots, to learning to defeat the human worldchampion 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, CarnegieMellon 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
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, MonteCarlo 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 realworld applications that range from learning to control robots, to learning to defeat the human worldchampion 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, CarnegieMellon 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
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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, MonteCarlo 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 realworld applications that range from learning to control robots, to learning to defeat the human worldchampion 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, CarnegieMellon 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
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, MonteCarlo 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 realworld applications that range from learning to control robots, to learning to defeat the human worldchampion 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, CarnegieMellon 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
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