Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning
2011
Article
ei
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.
Author(s): | Hachiya, H. and Peters, J. and Sugiyama, M. |
Journal: | Neural Computation |
Volume: | 23 |
Number (issue): | 11 |
Pages: | 2798-2832 |
Year: | 2011 |
Month: | November |
Day: | 0 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1162/NECO_a_00199 |
Links: |
Web
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BibTex @article{HachiyaPS2011, title = {Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning}, author = {Hachiya, H. and Peters, J. and Sugiyama, M.}, journal = {Neural Computation}, volume = {23}, number = {11}, pages = {2798-2832}, month = nov, year = {2011}, doi = {10.1162/NECO_a_00199}, month_numeric = {11} } |