TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-constrained Domains - Couverture rigide
Synopsis
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains.
Edité par
Cham, Springer., 2013
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
Ancien ou d'occasion
Couverture rigide
Vendeur : Universitätsbuchhandlung Herta Hold GmbH, Berlin, Allemagne
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XIII, 165 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped. Studies in Computational Intelligence, Vol. 503. Sprache: Englisch. N° de réf. du vendeur 3822FB
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (Studies in Computational Intelligence, 503)
Vendeur : PAPER CAVALIER UK, London, Royaume-Uni
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (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 7fc8ca4e5406b8517539bb7c5eb70c67
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (Studies in Computational Intelligence, 503)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In. N° de réf. du vendeur ria9783319011677_new
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Edité par
Springer International Publishing Jul 2013, 2013
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time.Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent's lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples. 180 pp. Englisch. N° de réf. du vendeur 9783319011677
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Edité par
Springer International Publishing, 2013
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Latest research on Temporal Difference Reinforcement Learning for Robots Focuses on applying Reinforcement Learning to real-world problems, particularly learning on robots Presents the model-based Reinforcement Learning algorithm developed . N° de réf. du vendeur 4496071
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Hardcover. Etat : Brand New. 2013 edition. 200 pages. 9.20x6.30x0.60 inches. In Stock. N° de réf. du vendeur x-3319011677
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
impression à la demandeVendeur : preigu, Osnabrück, Allemagne
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Buch. Etat : Neu. TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains | Todd Hester | Buch | xiv | Englisch | 2013 | Springer | EAN 9783319011677 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 105754366
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Neuware -This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time.Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent¿s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch. N° de réf. du vendeur 9783319011677
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TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Edité par
Springer, Springer, 2013
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time.Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent's lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples. N° de réf. du vendeur 9783319011677
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