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Classifier-based policy representation

Rexakis Ioannis, Lagoudakis Michael

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/2E55B7D4-6FCA-4907-8055-F24FEEF56CC9-
Αναγνωριστικόhttps://doi.org/10.1109/ICMLA.2008.31-
Γλώσσαen-
Μέγεθος8 pagesen
ΤίτλοςClassifier-based policy representationen
ΔημιουργόςRexakis Ioannisen
ΔημιουργόςΡεξακης Ιωαννηςel
ΔημιουργόςLagoudakis Michaelen
ΔημιουργόςΛαγουδακης Μιχαηλel
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηMotivated by recent proposals that view a reinforcement learning problem as a collection of classification problems, we investigate various aspects of policy representation using classifiers. In particular, we derive optimal policies for two standard reinforcement learning domains (inverted pendulum and mountain car) in both deterministic and stochastic versions and we examine their internal structure. We then proceed in an evaluation of the representational ability of a variety of classifiers for these policies, using both a multi-class and a binary formulation of the classification problem. Finally, we evaluate the actual performance of the policies learned by the classifiers in the original control problem as a function of the amount of training examples provided. Our results offer significant insight in making the reinforcement-learning-via-classification technology successfully applicable to hard learning problems.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-13-
Ημερομηνία Δημοσίευσης2008-
Θεματική ΚατηγορίαMachine Learningen
Βιβλιογραφική ΑναφοράI. Rexakis and M. G. Lagoudakis, “Classifier-Based Policy Representation,” in 2008 IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 91–98. doi:10.1109/ICMLA.2008.31en

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