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

Kamal, Md. Abdus Samad

ISBN 10: 3846555711 / ISBN 13: 9783846555712

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS.

Seller Inventory # ERICA80038465557116

Bibliographic Details

• Title: Efficient Reinforcement Learning in High Dimensional Domains: An approach to solve complex real world and engineeing problems
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3846555711
• ISBN 13: 9783846555712
• Binding: paperback
• Condition: Like New
• Book Type: book

About this title

Synopsis

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.

About the Author

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.