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

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

2004-08-11

arxiv.org/abs/math/0408146v1

Mathematics

General Mathematics

Writing date : July 30 2004 Submitted to the European Journal of Operation Research

Scientific paper

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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