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  Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation

Hachiya, H., Akiyama, T., Sugiyama, M., & Peters, J. (2008). Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation. In D. Fox, & C. Gomes (Eds.), Twenty-Third Conference on Artificial Intelligence 2008 (pp. 1351-1356). Menlo Park, CA, USA: AAAI Press.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C837-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-3D7F-C
Genre: Konferenzbeitrag

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externe Referenz:
https://www.aaai.org/Papers/AAAI/2008/AAAI08-214.pdf (Verlagsversion)
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Urheber

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 Urheber:
Hachiya, H, Autor           
Akiyama, T, Autor
Sugiyama, M, Autor
Peters, J1, 2, Autor           
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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Schlagwörter: -
 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.

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 Datum: 2008-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 5096
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Third Conference on Artificial Intelligence 2008
Veranstaltungsort: Chicago, IL, USA
Start-/Enddatum: 2008-07-13 - 2008-07-17

Entscheidung

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Projektinformation

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Quelle 1

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Titel: Twenty-Third Conference on Artificial Intelligence 2008
Genre der Quelle: Konferenzband
 Urheber:
Fox, D, Herausgeber
Gomes, CP, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Menlo Park, CA, USA : AAAI Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1351 - 1356 Identifikator: ISBN: 978-1-57735-368-3