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.
| Author(s): | Hachiya, H. and Peters, J. and Sugiyama, M. |
| Book Title: | 16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
| Journal: | Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2009 |
| Pages: | 469-484 |
| Year: | 2009 |
| Month: | September |
| Day: | 0 |
| Editors: | Buntine, W. , M. Grobelnik, D. Mladenic, J. Shawe-Taylor |
| Publisher: | Springer |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/978-3-642-04180-8_48 |
| Event Name: | ECML PKDD 2009 |
| Event Place: | Bled, Slovenia |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{6068,
title = {Efficient Sample Reuse in EM-Based Policy Search},
journal = {Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2009},
booktitle = {16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
abstract = {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.},
pages = {469-484},
editors = {Buntine, W. , M. Grobelnik, D. Mladenic, J. Shawe-Taylor},
publisher = {Springer},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Berlin, Germany},
month = sep,
year = {2009},
slug = {6068},
author = {Hachiya, H. and Peters, J. and Sugiyama, M.},
month_numeric = {9}
}