Hachiya, H., Peters, J., Sugiyama, M.
Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning
Neural Computation, 23(11):2798-2832, November 2011 (article)
Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.
Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning
Neural Networks, 22(10):1399-1410, December 2009 (article)
Hachiya, H., Peters, J., Sugiyama, M.
Efficient Sample Reuse in EM-Based Policy Search
In 16th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pages: 469-484, (Editors: Buntine, W. , M. Grobelnik, D. Mladenic, J. Shawe-Taylor), Springer, Berlin, Germany, ECML PKDD, September 2009 (inproceedings)
Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.
Efficient data reuse in value function approximation
In IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pages: 8-15, IEEE Service Center, Piscataway, NJ, USA, IEEE ADPRL, May 2009 (inproceedings)
Hachiya, H., Akiyama, T., Sugiyama, M., Peters, J.
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
In AAAI 2008, pages: 1351-1356, (Editors: Fox, D. , C. P. Gomes), AAAI Press, Menlo Park, CA, USA, Twenty-Third Conference on Artificial Intelligence, July 2008 (inproceedings)