Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model-Reference-Cited by-同舟云学术

Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model

Published:2013-05-14 Issue:3 Volume:91 Page:325-349
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Gheshlaghi Azar Mohammad,Munos Rémi,Kappen Hilbert J.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s10994-013-5368-1.pdf

Reference25 articles.

1. Azar, M. G., Munos, R., Ghavamzadeh, M., & Kappen, H. J. (2011a). Reinforcement learning with a near optimal rate of convergence. Tech. rep. http://hal.inria.fr/inria-00636615 .

2. Azar, M. G., Munos, R., Ghavamzadeh, M., & Kappen, H. J. (2011b). Speedy Q-learning. In Advances in neural information processing systems (Vol. 24, pp. 2411–2419).

3. Azar, M. G., Munos, R., Kappen, H. J. (2012). On the sample complexity of reinforcement learning with a generative model. In ICML. Omnipress.

4. Bartlett, P. L., & Tewari, A. (2009). REGAL: a regularization based algorithm for reinforcement learning in weakly communicating MDPs. In Proceedings of the 25th conference on uncertainty in artificial intelligence (pp. 35–42).

5. Bertsekas, D. P. (2007). Dynamic programming and optimal control (Vol. II, 3rd edn.). Belmount: Athena Scientific.

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