Learning a Machine for the Decision in a Partially Observable Markov Universe

Frederic Dambreville

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Learning a Machine for the Decision in a Partially Observable Markov Universe

Frederic Dambreville

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Abstract

In this paper, we are interested in optimal decisions in a partially observable Markov universe. Our viewpoint departs from the dynamic programming viewpoint: we are directly approximating an optimal strategic tree depending on the observation. This approximation is made by means of a parameterized probabilistic law. In this paper, a particular family of hidden Markov models, with input and output, is considered as a learning framework. A method for optimizing the parameters of these HMMs is proposed and applied. This optimization method is based on the cross-entropic principle.

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BibTeX RIS

Frederic Dambreville. 2004-08-11. Learning a Machine for the Decision in a Partially Observable Markov Universe. https://arxiv.org/abs/math/0408146

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