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Reference TypeConference Proceedings
Author(s)Peters, J.;Schaal, S.
Year2006
TitlePolicy gradient methods for robotics
Journal/Conference/Book TitleProceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006)
Keywordspolicy gradient methods, reinforcement learning, robotics
AbstractThe aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-structured environments. However, to date only few existing reinforcement learning methods have been scaled into the domains of highdimensional robots such as manipulator, legged or humanoid robots. Policy gradient methods remain one of the few exceptions and have found a variety of applications. Nevertheless, the application of such methods is not without peril if done in an uninformed manner. In this paper, we give an overview on learning with policy gradient methods for robotics with a strong focus on recent advances in the field. We outline previous applications to robotics and show how the most recently developed methods can significantly improve learning performance. Finally, we evaluate our most promising algorithm in the application of hitting a baseball with an anthropomorphic arm.
Place PublishedBeijing, Oct. 9-15
Short TitlePolicy gradient methods for robotics
URL(s) http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf

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