Overview
Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 173)
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About this book
Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement).
Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.
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Table of contents (8 chapters)
Editors and Affiliations
Bibliographic Information
Book Title: Reinforcement Learning
Editors: Richard S. Sutton
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4615-3618-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1992
Hardcover ISBN: 978-0-7923-9234-7Published: 31 May 1992
Softcover ISBN: 978-1-4613-6608-9Published: 08 October 2012
eBook ISBN: 978-1-4615-3618-5Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: 172
Topics: Artificial Intelligence, Complex Systems, Statistical Physics and Dynamical Systems