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  Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning

Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J. (2009). Adaptive Importance Sampling for Value Function Approximation in Off-policy Reinforcement Learning. Neural networks, 22(10), 1399-1410. doi:10.1016/j.neunet.2009.01.002.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C1B6-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-BAE1-E
Genre: Journal Article

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Hachiya, H, Author              
Akiyama, T, Author
Sugiyama, M, Author
Peters, J1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Off-policy reinforcement learning is aimed at efficiently using data samples gathered from a policy that is different from the currently optimized policy. A common approach is to use importance sampling techniques for compensating for the bias of value function estimators caused by the difference between the data-sampling policy and the target policy. However, existing off-policy methods often do not take the variance of the 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.

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 Dates: 2009-12
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neunet.2009.01.002
BibTex Citekey: 5530
 Degree: -

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Title: Neural networks
Source Genre: Journal
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Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 22 (10) Sequence Number: - Start / End Page: 1399 - 1410 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496