Articles liés à TEXPLORE: Temporal Difference Reinforcement Learning...
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Résultats de recherche pour TEXPLORE: Temporal Difference Reinforcement Learning...
Image d'archives
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (eng)
impression à la demandeVendeur : Brook Bookstore On Demand, Napoli, NA, Italie
Évaluation du vendeur 5 sur 5 étoiles
Etat : new. Questo è un articolo print on demand. N° de réf. du vendeur 7fc8ca4e5406b8517539bb7c5eb70c67
Acheter neuf
EUR 86,24
Expédition à EUR 5,50
Expédition depuis Italie vers Etats-Unis
Expédition depuis Italie vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
TEXPLORE : Temporal Difference Reinforcement Learning for Robots and Time-constrained Domains
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 20096721-n
Acheter neuf
EUR 127,23
Expédition à EUR 2,36
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 15 disponible(s)
Image d'archives
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (Studies in Computational Intelligence, 503)
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9783319011677
Acheter neuf
EUR 129,67
Livraison gratuite
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
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
Acheter neuf
EUR 106,99
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
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 5 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
Acheter neuf
EUR 93
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image d'archives
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains (Studies in Computational Intelligence, 503)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In English. N° de réf. du vendeur ria9783319011677_new
Acheter neuf
EUR 129,67
Expédition à EUR 13,32
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
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
Acheter neuf
EUR 122,01
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
Texplore
Vendeur : Books Puddle, Woodside, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. pp. 180. N° de réf. du vendeur 2651450799
Acheter neuf
EUR 157,36
Expédition à EUR 3,56
Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Edité par
Springer, Palgrave Macmillan Jul 2013, 2013
ISBN 10 : 3319011677
ISBN 13 : 9783319011677
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 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 KG, Sachsenplatz 4-6, 1201 Wien 180 pp. Englisch. N° de réf. du vendeur 9783319011677
Acheter neuf
EUR 106,99
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image d'archives
TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Hardcover. Etat : Brand New. 2013 edition. 200 pages. 9.20x6.30x0.60 inches. In Stock. N° de réf. du vendeur x-3319011677
Acheter neuf
EUR 156,83
Expédition à EUR 11,80
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
Quantité disponible : 2 disponible(s)