Efficient Reinforcement Learning in High Dimensional Domains: An approach to solve complex real world and engineeing problems - Softcover

Kamal, Md. Abdus Samad

 
9783846555712: Efficient Reinforcement Learning in High Dimensional Domains: An approach to solve complex real world and engineeing problems
Softcover
ISBN 10: 3846555711 ISBN 13: 9783846555712
Verlag: LAP LAMBERT Academic Publishing, 2011

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Dr. 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 interests include reinforcement learning, intelligent control systems, model predictive control and intelligent transportation systems.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2011
Sprache
Englisch
ISBN 10 
3846555711
ISBN 13 
9783846555712
Einband
Taschenbuch
Anzahl der Seiten
96
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
JustFiction Edition
customerservice@vdm-vsg.de

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Riga
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