Articles liés à Adaptive Representations for Reinforcement Learning
Synopsis
Presenting the main results of new algorithms for reinforcement learning, this book also introduces a novel method for devising input representations as well as presenting a way to find a minimal set of features sufficient to describe the agent's current state.
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Adaptive Representations for Reinforcement Learning (eng)
impression à la demandeVendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur d81d4855b92c9a10a96232493e9ca9e5
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Adaptive Representations for Reinforcement Learning
Edité par
Springer Berlin Heidelberg Okt 2010, 2010
ISBN 10 : 3642139310
ISBN 13 : 9783642139314
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations.The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements.This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations. 132 pp. Englisch. N° de réf. du vendeur 9783642139314
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Adaptive Representations for Reinforcement Learning (Studies in Computational Intelligence, 291)
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Adaptive Representations for Reinforcement Learning
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Adaptive Representations for Reinforcement Learning
Edité par
Springer Berlin Heidelberg, 2010
ISBN 10 : 3642139310
ISBN 13 : 9783642139314
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Adaptive Representations for Reinforcement LearningWritten by leading experts in this fieldState-of-the Art bookThis book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seek. N° de réf. du vendeur 5050346
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Adaptive Representations for Reinforcement Learning
Edité par
Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2010
ISBN 10 : 3642139310
ISBN 13 : 9783642139314
Neuf
Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
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Hardback. Etat : New. 2011th. This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations. N° de réf. du vendeur LU-9783642139314
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Adaptive Representations for Reinforcement Learning
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations.The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements.This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations. N° de réf. du vendeur 9783642139314
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Adaptive Representations for Reinforcement Learning
Edité par
Springer, Springer Okt 2010, 2010
ISBN 10 : 3642139310
ISBN 13 : 9783642139314
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations.The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements.This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch. N° de réf. du vendeur 9783642139314
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Adaptive Representations for Reinforcement Learning
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Hardcover. Etat : Brand New. 133 pages. 9.00x6.25x0.25 inches. In Stock. N° de réf. du vendeur x-3642139310
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Adaptive Representations for Reinforcement Learning
Edité par
Springer Spektrum, 2010
ISBN 10 : 3642139310
ISBN 13 : 9783642139314
Ancien ou d'occasion
Couverture rigide
Vendeur : Buchpark, Trebbin, Allemagne
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Etat : Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class ofoptimization problems. This synthesis is accomplished by customizing evolutionarymethods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methodswith manual representations. N° de réf. du vendeur 7906270/2
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