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Policy Gradient Methods for Robotics

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Peters, J. (2006). Policy Gradient Methods for Robotics. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2219-2225). Los Alamitos, CA, USA: IEEE Computer Society.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CFD3-6
Zusammenfassung
Abstract—The 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.