Recent Advances in Reinforcement Learning

Leslie Pack Kaelbling

ISBN 10: 0792397053 / ISBN 13: 9780792397052

Published by Springer US, Springer US, 1996

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

Seller Inventory # 9780792397052

Bibliographic Details

• Title: Recent Advances in Reinforcement Learning
• Publisher: Springer US, Springer US
• Publication Date: 1996
• Language: English
• ISBN 10: 0792397053
• ISBN 13: 9780792397052
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 22D millimeters 241 millimeters width by 160 millimeters height by 22 millimeters depth
• Weight: 610 grams

About this title

Synopsis

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities.
Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area.
Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).