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Efficient Sample Reuse in EM-Based Policy Search

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83950

Hachiya,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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

Hachiya, H., Peters, J., & Sugiyama, M. (2009). Efficient Sample Reuse in EM-Based Policy Search. Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2009, 469-484.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C307-1
Zusammenfassung
Direct policy search is a promising reinforcement learning framework in particular for controlling in continuous, high-dimensional systems such as anthropomorphic robots. Policy search often requires a large number of samples for obtaining a stable policy update estimator due to its high flexibility. However, this is prohibitive when the sampling cost is expensive. In this paper, we extend a EM-based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, called Reward-weighted Regression with sample Reuse, is demonstrated through a robot learning experiment.