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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Introductory Part

Frontmatter

2012 | OriginalPaper | Buchkapitel

Reinforcement Learning and Markov Decision Processes

Martijn van Otterlo, Marco Wiering

Efficient Solution Frameworks

Frontmatter

2012 | OriginalPaper | Buchkapitel

Batch Reinforcement Learning

Sascha Lange, Thomas Gabel, Martin Riedmiller

2012 | OriginalPaper | Buchkapitel

Least-Squares Methods for Policy Iteration

Lucian Buşoniu, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Robert Babuška, Bart De Schutter

2012 | OriginalPaper | Buchkapitel

Learning and Using Models

Todd Hester, Peter Stone

2012 | OriginalPaper | Buchkapitel

Transfer in Reinforcement Learning: A Framework and a Survey

Alessandro Lazaric

2012 | OriginalPaper | Buchkapitel

Sample Complexity Bounds of Exploration

Lihong Li

Constructive-Representational Directions

Frontmatter

2012 | OriginalPaper | Buchkapitel

Reinforcement Learning in Continuous State and Action Spaces

Hado van Hasselt

2012 | OriginalPaper | Buchkapitel

Solving Relational and First-Order Logical Markov Decision Processes: A Survey

Martijn van Otterlo

2012 | OriginalPaper | Buchkapitel

Hierarchical Approaches

Bernhard Hengst

2012 | OriginalPaper | Buchkapitel

Evolutionary Computation for Reinforcement Learning

Shimon Whiteson

Probabilistic Models of Self and Others

Frontmatter

2012 | OriginalPaper | Buchkapitel

Bayesian Reinforcement Learning

Nikos Vlassis, Mohammad Ghavamzadeh, Shie Mannor, Pascal Poupart

2012 | OriginalPaper | Buchkapitel

Partially Observable Markov Decision Processes

Matthijs T. J. Spaan

2012 | OriginalPaper | Buchkapitel

Predictively Defined Representations of State

David Wingate

2012 | OriginalPaper | Buchkapitel

Game Theory and Multi-agent Reinforcement Learning

Ann Nowé, Peter Vrancx, Yann-Michaël De Hauwere

2012 | OriginalPaper | Buchkapitel

Decentralized POMDPs

Frans A. Oliehoek

Domains and Background

Frontmatter

2012 | OriginalPaper | Buchkapitel

Psychological and Neuroscientific Connections with Reinforcement Learning

Ashvin Shah

2012 | OriginalPaper | Buchkapitel

Reinforcement Learning in Games

István Szita

2012 | OriginalPaper | Buchkapitel

Reinforcement Learning in Robotics: A Survey

Jens Kober, Jan Peters

Closing

Frontmatter

2012 | OriginalPaper | Buchkapitel

Conclusions, Future Directions and Outlook

Marco Wiering, Martijn van Otterlo

Backmatter

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