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  Bootstrapping Apprenticeship Learning

Boularias, A., & Chaib-Draa, B. (2011). Bootstrapping Apprenticeship Learning. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, 289-297.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB72-C Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-0AAA-4
Genre: Konferenzbeitrag

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externe Referenz:
https://papers.nips.cc/paper/4160-bootstrapping-apprenticeship-learning.pdf (Verlagsversion)
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 Urheber:
Boularias, A1, 2, Autor           
Chaib-Draa, B, 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: We consider the problem of apprenticeship learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is maximizing a utility function that is a linear combination of state-action features. Most IRL algorithms use a simple Monte Carlo estimation to approximate the expected feature counts under the expert's policy. In this paper, we show that the quality of the learned policies is highly sensitive to the error in estimating the feature counts. To reduce this error, we introduce a novel approach for bootstrapping the demonstration by assuming that: (i), the expert is (near-)optimal, and (ii), the dynamics of the system is known. Empirical results on gridworlds and car racing problems show that our approach is able to learn good policies from a small number of demonstrations.

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 Datum: 2011-06
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 6826
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010)
Veranstaltungsort: Vancouver, BC, Canada
Start-/Enddatum: 2010-12-06 - 2010-12-11

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Quelle 1

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Titel: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010
Genre der Quelle: Zeitschrift
 Urheber:
Lafferty, J, Herausgeber
Williams, CKI, Herausgeber
Shawe-Taylor, J, Herausgeber
Zemel, RS, Herausgeber
Culotta, A, Herausgeber
Affiliations:
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Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 289 - 297 Identifikator: ISBN: 978-1-617-82380-0