Overview
- Covers all important recent developments in reinforcement learning
- Very good introduction and explanation of the different emerging areas in Reinforcement Learning
- Includes a survey of previous papers written on the topic
Part of the book series: Adaptation, Learning, and Optimization (ALO, volume 12)
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Table of contents (19 chapters)
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Front Matter
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Introductory Part
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Front Matter
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Constructive-Representational Directions
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Front Matter
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Probabilistic Models of Self and Others
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Front Matter
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Keywords
About this book
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.
The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.
Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge
representation in reinforcement learning settings.
Editors and Affiliations
Bibliographic Information
Book Title: Reinforcement Learning
Book Subtitle: State-of-the-Art
Editors: Marco Wiering, Martijn Otterlo
Series Title: Adaptation, Learning, and Optimization
DOI: https://doi.org/10.1007/978-3-642-27645-3
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-27644-6Published: 14 March 2012
Softcover ISBN: 978-3-642-44685-6Published: 16 April 2014
eBook ISBN: 978-3-642-27645-3Published: 05 March 2012
Series ISSN: 1867-4534
Series E-ISSN: 1867-4542
Edition Number: 1
Number of Pages: XXXIV, 638