Approximate policy iteration using large-margin classifiers

URI	http://purl.tuc.gr/dl/dias/B95FD666-3683-44DB-8681-8CB3C2DFEC7B	-
Language	en	-
Extent	3 pages	en
Title	Approximate policy iteration using large-margin classifiers	en
Creator	Lagoudakis Michael	en
Creator	Λαγουδακης Μιχαηλ	el
Content Summary	We 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 Item	Πλήρης Δημοσίευση σε Συνέδριο	el
Type of Item	Conference Full Paper	en
License	http://creativecommons.org/licenses/by/4.0/	en
Date of Item	2015-11-13	-
Date of Publication	2003	-
Subject	Artificial Intelligence	en
Bibliographic Citation	M.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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