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dc.contributor.authorAgostini, Alejandro-
dc.contributor.authorCelaya, Enric-
dc.identifierisbn: 978-1-57735-514-4-
dc.identifier.citationInternational Joint Conference on Artificial Intelligence 2: 1146-1151 (2011)-
dc.descriptionTrabajo presentado al 22nd IJCAI celebrado en Barcelona del 16 al 22 de julio de 2011.-
dc.description.abstractIn this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one defined in a different region of the domain. Associated with each approximator is a relevance function that locally quantifies the quality of its approximation, so that, at each input point, the approximator with highest relevance can be selected. The relevance function is defined using parametric estimations of the variance of the q-values and the density of samples in the input space, which are used to quantify the accuracy and the confidence in the approximation, respectively. These parametric estimations are obtained from a probability density distribution represented as a Gaussian Mixture Model embedded in the input-output space of each approximator. In our experiments, the proposed approach required a lesser number of experiences for learning and produced more stable convergence profiles than when using a single function approximator.-
dc.description.sponsorshipThis research was partially supported by Consolider Ingenio 2010, project CSD2007-00018.-
dc.publisherAAAI Press-
dc.titleA competitive strategy for function approximation in Q-learning-
dc.typecomunicación de congreso-
dc.description.versionPeer Reviewed-
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