Learning a Machine for the Decision in a Partially Observable Markov Universe
<|reference_start|>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 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.<|reference_end|>
@article{dambreville2004learning,
    title={Learning a Machine for the Decision in a Partially Observable Markov
  Universe},
    author={Frederic Dambreville (DGA/CTA/DT/GIP)},
    journal={arXiv preprint arXiv:math/0408146},
    year={2004},
    archivePrefix={arXiv},
    eprint={math/0408146},
    primaryClass={math.GM cs.AI cs.LG}
}