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Reinforcement Learning pp 579–610Cite as

Reinforcement Learning in Robotics: A Survey

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

Abstract

As most action generation problems of autonomous robots can be phrased in terms of sequential decision problems, robotics offers a tremendously important and interesting application platform for reinforcement learning. Similarly, the real-world challenges of this domain pose a major real-world check for reinforcement learning. Hence, the interplay between both disciplines can be seen as promising as the one between physics and mathematics. Nevertheless, only a fraction of the scientists working on reinforcement learning are sufficiently tied to robotics to oversee most problems encountered in this context. Thus, we will bring the most important challenges faced by robot reinforcement learning to their attention. To achieve this goal, we will attempt to survey most work that has successfully applied reinforcement learning to behavior generation for real robots. We discuss how the presented successful approaches have been made tractable despite the complexity of the domain and will study how representations or the inclusion of prior knowledge can make a significant difference. As a result, a particular focus of our chapter lies on the choice between model-based and model-free as well as between value function-based and policy search methods. As a result, we obtain a fairly complete survey of robot reinforcement learning which should allow a general reinforcement learning researcher to understand this domain.

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  1. Intelligent Autonomous Systems Institute, Technische Universitaet Darmstadt, Darmstadt, Germany

    Jens Kober & Jan Peters

  2. Robot Learning Lab, Max-Planck Institute for Intelligent Systems, Tübingen, Germany

    Jens Kober & Jan Peters

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  1. Jens Kober
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  1. Fac. Mathematics &, Natural Sciences, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands

    Marco Wiering

  2. , Artificial Intelligence, Radboud University Nijmegen, B.02.30 Spinozagebouw, Montessorilaan 3, Nijmegen, 6500, Netherlands

    Martijn van Otterlo

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© 2012 Springer-Verlag Berlin Heidelberg

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Kober, J., Peters, J. (2012). Reinforcement Learning in Robotics: A Survey. In: Wiering, M., van Otterlo, M. (eds) Reinforcement Learning. Adaptation, Learning, and Optimization, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27645-3_18

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  • DOI: https://doi.org/10.1007/978-3-642-27645-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27644-6

  • Online ISBN: 978-3-642-27645-3

  • eBook Packages: EngineeringEngineering (R0)

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