Adaptation, Learning, and Optimization

Reinforcement Learning

State-of-the-Art

Editors: Wiering, Marco, van Otterlo, Martijn (Eds.)

  • 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
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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.

Table of contents (19 chapters)

  • Reinforcement Learning and Markov Decision Processes

    Otterlo, Martijn (et al.)

    Pages 3-42

  • Batch Reinforcement Learning

    Lange, Sascha (et al.)

    Pages 45-73

  • Least-Squares Methods for Policy Iteration

    Buşoniu, Lucian (et al.)

    Pages 75-109

  • Learning and Using Models

    Hester, Todd (et al.)

    Pages 111-141

  • Transfer in Reinforcement Learning: A Framework and a Survey

    Lazaric, Alessandro

    Pages 143-173

Buy this book

eBook $259.00
price for USA (gross)
  • ISBN 978-3-642-27645-3
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $329.00
price for USA
  • ISBN 978-3-642-27644-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $329.00
price for USA
  • ISBN 978-3-642-44685-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the ebook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Reinforcement Learning
Book Subtitle
State-of-the-Art
Editors
  • Marco Wiering
  • Martijn van Otterlo
Series Title
Adaptation, Learning, and Optimization
Series Volume
12
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-27645-3
DOI
10.1007/978-3-642-27645-3
Hardcover ISBN
978-3-642-27644-6
Softcover ISBN
978-3-642-44685-6
Series ISSN
1867-4534
Edition Number
1
Number of Pages
XXXIV, 638
Topics