Reinforcement Learning

Cornelius Weber

ISBN 10: 3902613149 / ISBN 13: 9783902613141

Published by Intechopen Jan 2008, 2008

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal.The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field. 434 pp. Englisch.

Seller Inventory # 9783902613141

Bibliographic Details

• Title: Reinforcement Learning
• Publisher: Intechopen Jan 2008
• Publication Date: 2008
• Language: English
• ISBN 10: 3902613149
• ISBN 13: 9783902613141
• Binding: Buch
• Condition: Neu
• Dimensions: 266W x 185H x 34D millimeters 266 millimeters width by 185 millimeters height by 34 millimeters depth
• Weight: 1146 grams

About this title

Synopsis

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.