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Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83950

Hachiya,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Hachiya, H., Akiyama T, Sugiyama, M., & Peters, J. (2008). Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. Proceedings of the Twenty-Third Conference on Artificial Intelligence (AAAI 2008), 1351-1356.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C837-7
Zusammenfassung
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are usually prohibitively expensive. A common approach is to use importance sampling techniques for compensating for the bias caused by the difference between data-sampling policies and the target policy. However, existing off-policy methods do not often take the variance of 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. We demonstrate the usefulness of the proposed approach through simulations.