Isbn: 9783846555712 - efficient reinforcement learning in high dimensional domains: an approach to solve complex real world and engineeing problems (9 resultados)

Condición: New. pp. 96.

Taschenbuch. Condición: Neu. Efficient Reinforcement Learning in High Dimensional Domains | An approach to solve complex real world and engineeing problems | Md. Abdus Samad Kamal | Taschenbuch | 96 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846555712 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents development of efficient reinforcement learning methods in a postgraduate research. A reinforcement learning agent tries every state-action pair to find the optimal policy without prior knowledge about the domain. In large domains visiting every state-action pair is not feasible by an agent, therefore standard reinforcement learning approach is not applicable in solving many real world problems. Three new methods are proposed to make the learning efficient according to the characteristics of the problems: Task-Oriented Reinforcement Learning reduces the problem size by viewing it from the task's viewpoint that clarifies task relevant state variables. Symmetrical-Actions Reinforcement Leaning reduces the size of a learning problem by exploiting partial symmetry over action relevant state variables and representing actions values by a single function. Coordinated Multiagent Reinforcement Learning technique uses coordinator-agent hierarchy to keep the size of individual learning problems small. Depending on problem characteristics all or any of these methods can be applied to solve a problem efficiently using reinforcement learning. 96 pp. Englisch.…

Condición: New. Print on Demand pp. 96 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 96.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kamal Md. Abdus SamadDr. Kamal studied in KUET, Bangladesh and Kyushu University, Japan. In his academic profession he worked in universities including KUET, Kyushu University, IIUM Malaysia and The University of Tokyo. His research .…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents development of efficient reinforcement learning methods in a postgraduate research. A reinforcement learning agent tries every state-action pair to find the optimal policy without prior knowledge about the domain. In large domains visiting every state-action pair is not feasible by an agent, therefore standard reinforcement learning approach is not applicable in solving many real world problems. Three new methods are proposed to make the learning efficient according to the characteristics of the problems: Task-Oriented Reinforcement Learning reduces the problem size by viewing it from the task's viewpoint that clarifies task relevant state variables. Symmetrical-Actions Reinforcement Leaning reduces the size of a learning problem by exploiting partial symmetry over action relevant state variables and representing actions values by a single function. Coordinated Multiagent Reinforcement Learning technique uses coordinator-agent hierarchy to keep the size of individual learning problems small. Depending on problem characteristics all or any of these methods can be applied to solve a problem efficiently using reinforcement learning.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents development of efficient reinforcement learning methods in a postgraduate research. A reinforcement learning agent tries every state-action pair to find the optimal policy without prior knowledge about the domain. In large domains visiting every state-action pair is not feasible by an agent, therefore standard reinforcement learning approach is not applicable in solving many real world problems. Three new methods are proposed to make the learning efficient according to the characteristics of the problems: Task-Oriented Reinforcement Learning reduces the problem size by viewing it from the task's viewpoint that clarifies task relevant state variables. Symmetrical-Actions Reinforcement Leaning reduces the size of a learning problem by exploiting partial symmetry over action relevant state variables and representing actions values by a single function. Coordinated Multiagent Reinforcement Learning technique uses coordinator-agent hierarchy to keep the size of individual learning problems small. Depending on problem characteristics all or any of these methods can be applied to solve a problem efficiently using reinforcement learning.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.…