Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe
Optimization and Control
2007-06-13 v1 Artificial Intelligence
Machine Learning
Neural and Evolutionary Computing
Robotics
Statistics Theory
Statistics Theory
Abstract
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.
Keywords
Cite
@article{arxiv.math/0605498,
title = {Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe},
author = {Frederic Dambreville},
journal= {arXiv preprint arXiv:math/0605498},
year = {2007}
}
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