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Number of items: 8.

Hachiya, H. and Peters, J. and Sugiyama, M. (2011):
Reward Weighted Regression with Sample Reuse.
In: Neural Computation, (23(11)), pp. 2798-2832. [Article]

Hachiya, H. and Peters, J. and Sugiyama, M. (2011):
Reward Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning.
In: Neural Computation, 23 (11), pp. 2798-2832. MIT Press, [Article]

Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J. (2009):
Adaptive Importance Sampling for Value Function Approximation in On-policy Reinforcement Learning.
In: doi: 10.1016/j.neunet.2009.01.002, In: Neural Networks, 22(10), pp.1399-1410, [Article]

Hachiya, H. and Peters, J. and Sugiyama, M. (2009):
Adaptive Importance Sampling with Automatic Model Selection in Reward Weighted Regression.
In: Winner of the IEEE CIS Japan Chapter YRA Award, Proceedings of the Workshop of Technical Committee on Neurocomputing, [Conference or Workshop Item]

Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J. (2009):
Efficient Data Reuse in Value Function Approximation.
Proceedings of the 2009 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, [Conference or Workshop Item]

Hachiya, H. and Peters, J. and Sugiyama, M. (2009):
Efficient Sample Reuse in EM-based Policy Search.
In: Acceptance Rate: 24%, Proceedings of the 16th European Conference on Machine Learning (ECML), [Conference or Workshop Item]

Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J. (2008):
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation.
In: Acceptance Rate: 24%, Proceedings of the Twenty-Third National Conference on Artificial Intelligence (AAAI), [Conference or Workshop Item]

Hachiya, H. and Akiyama, T. and Sugiyama, M. and Peters, J. (2008):
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation.
Conference on Artificial Intelligence (AAAI 2008), Chicago, Illinois, July 13–17, 2008, [Conference or Workshop Item]

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