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  Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning

Hachiya, H., Peters, J., & Sugiyama, M. (2011). Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning. Neural computation, 23(11), 2798-2832. doi:10.1162/NECO_a_00199.

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Hachiya, H, Author              
Peters, J1, 2, Author              
Sugiyama, M, Author
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Direct policy search is a promising reinforcement learning framework, in particular for controlling continuous, high-dimensional systems. Policy search often requires a large number of samples for obtaining a stable policy update estimator, and this is prohibitive when the sampling cost is expensive. In this letter, we extend an expectation-maximization-based policy search method so that previously collected samples can be efficiently reused. The usefulness of the proposed method, reward-weighted regression with sample reuse (R), is demonstrated through robot learning experiments.

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 Dates: 2011-11
 Publication Status: Published in print
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 Identifiers: DOI: 10.1162/NECO_a_00199
BibTex Citekey: HachiyaPS2011
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 23 (11) Sequence Number: - Start / End Page: 2798 - 2832 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591