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Reinforcement Learning in Robotics: A Survey

  • Jens KoberEmail author
  • Jan Peters
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 97)

Summary

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 chapter lies on the choice between model-based 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

Mobile Robot Reinforcement Learning Reward Function Real Robot Policy Iteration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CoR-Lab Universität BielefeldBielefeldGermany
  2. 2.Fachgebiet Intelligente Autonome Systeme Technische Universität DarmstadtDarmstadtGermany

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