Reinforcement Learning in Robotics: A Survey

J. Kober, J. Andrew (Drew) Bagnell, and J. Peters
International Journal of Robotics Research, , July, 2013


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Abstract
Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between modelbased and model-free as well as between value function-based and policy search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

Keywords
reinforcement learning, learning control, robot, survey

Notes
Number of pages: 73
Note: accepted

Text Reference
J. Kober, J. Andrew (Drew) Bagnell, and J. Peters, "Reinforcement Learning in Robotics: A Survey," International Journal of Robotics Research, , July, 2013

BibTeX Reference
@article{Bagnell_2013_7451,
   author = "J. Kober and J. Andrew (Drew) Bagnell and J. Peters",
   title = "Reinforcement Learning in Robotics: A Survey",
   journal = "International Journal of Robotics Research",
   month = "July",
   year = "2013",
   Notes = "accepted"
}