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Reinforcement Learning in Robotics: A Survey


Book Chapter


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.

Author(s): Kober, J. and Peters, J.
Book Title: Reinforcement Learning
Volume: 12
Pages: 579-610
Year: 2012
Day: 0
Editors: Wiering, M. and Otterlo, M.
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Book Chapter (inbook)

Address: Berlin, Germany
DOI: 10.1007/978-3-642-27645-3_18

Links: Web


  title = {Reinforcement Learning in Robotics: A Survey},
  author = {Kober, J. and Peters, J.},
  booktitle = {Reinforcement Learning},
  volume = {12},
  pages = {579-610},
  editors = {Wiering, M. and Otterlo, M.},
  publisher = {Springer},
  address = {Berlin, Germany},
  year = {2012}