Convergence and Divergence in Standard and Averaging Reinforcement Learning-Reference-Cited by-同舟云学术

Convergence and Divergence in Standard and Averaging Reinforcement Learning

Published:2004 Issue: Volume: Page:477-488
ISSN:0302-9743
Container-title:Machine Learning: ECML 2004
language:
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Author:

Wiering Marco A.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-30115-8_44.pdf

Reference18 articles.

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3. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton (1957)

4. Boyan, J.A., Moore, A.W.: Generalization in reinforcement learning: Safely approximating the value function. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 369–376. MIT Press, Cambridge (1995)

5. Gordon, G.J.: Stable function approximation in dynamic programming. Technical Report CMU-CS-95-103, Carnegie Mellon University (1995)

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