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Approximate policy iteration using large-margin classifiers

Lagoudakis Michael

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URIhttp://purl.tuc.gr/dl/dias/B95FD666-3683-44DB-8681-8CB3C2DFEC7B-
Languageen-
Extent3 pagesen
TitleApproximate policy iteration using large-margin classifiersen
CreatorLagoudakis Michaelen
CreatorΛαγουδακης Μιχαηλel
Content SummaryWe present an approximate policy iteration algorithm that uses rollouts to estimate the value of each action under a given policy in a subset of states and a classifier to generalize and learn the improved policy over the entire state space. Using a multiclass support vector machine as the classifier, we obtained successful results on the inverted pendulum and the bicycle balancing and riding domains.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-13-
Date of Publication2003-
SubjectArtificial Intelligenceen
Bibliographic CitationM.G. Lagoudakis and R. Parr, “Approximate policy iteration using large-margin classifiers,” in Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), 2003, pp. 1432–1434.en

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