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Policy Learning: A Unified Perspective with Applications in Robotics

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Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Kober,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Nguyen-Tuong,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Peters, J., Kober, J., & Nguyen-Tuong, D. (2008). Policy Learning: A Unified Perspective with Applications in Robotics. In S. Girgin, M. Loth, R. Munos, P. Preux, & D. Ryabko (Eds.), Recent Advances in Reinforcement Learning: 8th European Workshop, EWRL 2008, Villeneuve d’Ascq, France, June 30-July 3, 2008 (pp. 220-228). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C669-B
Abstract
Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning algorithms from a common point of view, i.e, policy gradient algorithms, natural-gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.