This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor.
skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor
skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Anbieter: Brook Bookstore On Demand, Napoli, NA, Italien
Zustand: new. Questo è un articolo print on demand. Bestandsnummer des Verkäufers 56192925f04caeb9907cdd1ffe99edf1
Anzahl: Mehr als 20 verfügbar
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Zustand: New. In. Bestandsnummer des Verkäufers ria9783319377322_new
Anzahl: Mehr als 20 verfügbar
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor.skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author's doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award. 208 pp. Englisch. Bestandsnummer des Verkäufers 9783319377322
Anzahl: 2 verfügbar
Anbieter: Books Puddle, New York, NY, USA
Zustand: New. pp. 207 Softcover reprint of the original 1st ed. 2014 edition NO-PA16APR2015-KAP. Bestandsnummer des Verkäufers 26378217799
Anzahl: 4 verfügbar
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
Paperback. Zustand: New. Bestandsnummer des Verkäufers 6666-IUK-9783319377322
Anzahl: 10 verfügbar
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. Print on Demand pp. 207. Bestandsnummer des Verkäufers 385653400
Anzahl: 4 verfügbar
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND pp. 207. Bestandsnummer des Verkäufers 18378217805
Anzahl: 4 verfügbar
Anbieter: moluna, Greven, Deutschland
Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents an overview of reinforcement learning as applied to roboticsProvides novel algorithms and novel applications for learning motor skillsExtensively evaluates the applications of the approaches on benchmark and robot tasks (including . Bestandsnummer des Verkäufers 448748558
Anzahl: Mehr als 20 verfügbar
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Learning Motor Skills | From Algorithms to Robot Experiments | Jens Kober (u. a.) | Taschenbuch | Springer Tracts in Advanced Robotics | xvi | Englisch | 2016 | Springer | EAN 9783319377322 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Bestandsnummer des Verkäufers 103411953
Anzahl: 5 verfügbar
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor.skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author¿s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch. Bestandsnummer des Verkäufers 9783319377322
Anzahl: 1 verfügbar