Reinforcement Learning


  • Marco Wiering
  • Martijn van Otterlo

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 12)

Table of contents

  1. Front Matter
    Pages 1-31
  2. Introductory Part

    1. Front Matter
      Pages 1-1
    2. Martijn van Otterlo, Marco Wiering
      Pages 3-42
  3. Efficient Solution Frameworks

    1. Front Matter
      Pages 43-43
    2. Sascha Lange, Thomas Gabel, Martin Riedmiller
      Pages 45-73
    3. Lucian Buşoniu, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Robert Babuška, Bart De Schutter
      Pages 75-109
    4. Todd Hester, Peter Stone
      Pages 111-141
    5. Lihong Li
      Pages 175-204
  4. Constructive-Representational Directions

    1. Front Matter
      Pages 205-205
    2. Bernhard Hengst
      Pages 293-323
  5. Probabilistic Models of Self and Others

    1. Front Matter
      Pages 357-357
    2. Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, Pascal Poupart
      Pages 359-386
    3. Matthijs T. J. Spaan
      Pages 387-414
    4. David Wingate
      Pages 415-439
    5. Ann Nowé, Peter Vrancx, Yann-Michaël De Hauwere
      Pages 441-470
    6. Frans A. Oliehoek
      Pages 471-503
  6. Domains and Background

    1. Front Matter
      Pages 505-505
    2. István Szita
      Pages 539-577
    3. Jens Kober, Jan Peters
      Pages 579-610
  7. Closing

    1. Front Matter
      Pages 611-611
    2. Marco Wiering, Martijn van Otterlo
      Pages 613-630
  8. Back Matter
    Pages 631-638

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.


Artificial Intelligence Computational Intelligence Decision-Theoretic Planning Dynamic Programming Machine Learning Markov Decision Processes Optimal Control Reinforcement Learning Utility-Based Learning

Editors and affiliations

  • Marco Wiering
    • 1
  • Martijn van Otterlo
    • 2
  1. 1.Fac. Mathematics &, Natural SciencesUniversity of GroningenGroningenNetherlands
  2. 2., Artificial IntelligenceRadboud University NijmegenNijmegenNetherlands

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-27644-6
  • Online ISBN 978-3-642-27645-3
  • Series Print ISSN 1867-4534
  • Series Online ISSN 1867-4542
  • Buy this book on publisher's site
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