Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization, 12)

ISBN 10: 364227644X ISBN 13: 9783642276446
Edité par Springer, 2012
Neuf(s) Couverture rigide

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This book presents up-to-date information on the main contemporary sub-fields of reinforcement learning, including partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations.

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Titre : Reinforcement Learning: State-of-the-Art (...
Éditeur : Springer
Date d'édition : 2012
Reliure : Couverture rigide
Etat : New

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Wiering, Marco|Otterlo, Martijn van
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers all important recent developments in reinforcement learningVery good introduction and explanation of the different emerging areas in Reinforcement LearningIncludes a survey of previous papers written on the topicCo. N° de réf. du vendeur 5055345

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledgerepresentation in reinforcement learning settings. N° de réf. du vendeur 9783642276446

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledgerepresentation in reinforcement learning settings. 672 pp. Englisch. N° de réf. du vendeur 9783642276446

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Buch. Etat : Neu. Neuware -Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledgerepresentation in reinforcement learning settings.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 672 pp. Englisch. N° de réf. du vendeur 9783642276446

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Edité par Springer, 2012
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Etat : New. N° de réf. du vendeur I-9783642276446

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Wiering, Marco (Editor)/ Van Otterlo, Martijn (Editor)
Edité par Springer-Verlag New York Inc, 2012
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Hardcover. Etat : Brand New. 2012 edition. 672 pages. 9.25x6.25x1.75 inches. In Stock. N° de réf. du vendeur x-364227644X

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