Overview
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Table of contents (12 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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).
Editors and Affiliations
Bibliographic Information
Book Title: Recent Advances in Reinforcement Learning
Editors: Leslie Pack Kaelbling
DOI: https://doi.org/10.1007/b102434
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1996
Hardcover ISBN: 978-0-7923-9705-2Published: 31 March 1996
Softcover ISBN: 978-1-4419-5160-1Published: 07 December 2010
eBook ISBN: 978-0-585-33656-5Published: 28 August 2007
Edition Number: 1
Number of Pages: IV, 292
Topics: Artificial Intelligence, Complex Systems, Computer Science, general, Statistical Physics and Dynamical Systems