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Pré-Publication, Document De Travail Année : 2006

Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe

Frederic Dambreville
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Résumé

Revision of the paper previously entitled "Learning a Machine for the Decision in a Partially Observable Markov Universe" In this paper, we are interested in optimal decisions in a partially observable universe. Our approach is to directly approximate an optimal strategic tree depending on the observation. This approximation is made by means of a parameterized probabilistic law. A particular family of hidden Markov models, with input \emph{and} output, is considered as a model of policy. A method for optimizing the parameters of these HMMs is proposed and applied. This optimization is based on the cross-entropic principle for rare events simulation developed by Rubinstein.
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Dates et versions

hal-00069493 , version 1 (18-05-2006)

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Frederic Dambreville. Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe. 2006. ⟨hal-00069493⟩

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