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  Reinforcement Learning in Robotics: A Survey

Kober, J., & Peters, J. (2012). Reinforcement Learning in Robotics: A Survey. In M. Wierig, & M. Otterlo (Eds.), Reinforcement Learning (pp. 579-610). Berlin, Germany: Springer.

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 Creators:
Kober, J1, Author           
Peters, J1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
 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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 Dates: 2012
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-27645-3_18
BibTex Citekey: KoberPM2012
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Title: Reinforcement Learning
Source Genre: Book
 Creator(s):
Wierig, M., Editor
Otterlo, M., Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: 12 Sequence Number: - Start / End Page: 579 - 610 Identifier: -

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Title: Adaption, Learning, and Optimization
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: Vol. 12 Sequence Number: - Start / End Page: - Identifier: -