Header logo is ei

Efficient data reuse in value function approximation

2009

Conference Paper

ei


Off-policy reinforcement learning is aimed at efficiently using data samples gathered from a policy that is different from the currently optimized policy. A common approach is to use importance sampling techniques for compensating for the bias of value function estimators caused by the difference between the data-sampling policy and the target policy. However, existing off-policy methods often do not take the variance of the value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a variant of cross-validation. The usefulness of the proposed approach is demonstrated through simulated swing-up inverted-pendulum problem.

Author(s): Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J.
Book Title: IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Journal: Proceedings of the 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL 2009)
Pages: 8-15
Year: 2009
Month: May
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ADPRL.2009.4927519
Event Name: IEEE ADPRL 2009
Event Place: Nashville, TN, USA

Address: Piscataway, NJ, USA
Institution: Institute of Electrical and Electronics Engineers
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{5771,
  title = {Efficient data reuse in value function approximation},
  author = {Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J.},
  journal = {Proceedings of the 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (IEEE ADPRL 2009)},
  booktitle = {IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning},
  pages = {8-15},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  institution = {Institute of Electrical and Electronics Engineers},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = may,
  year = {2009},
  month_numeric = {5}
}