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

Simple item page
dc.creatorDambreville, Frederic
dc.date2006-05-18
dc.date.accessioned2026-07-07T08:07:49Z
dc.date.available2026-07-07T08:07:49Z
dc.descriptionRevision 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.
dc.descriptionSubmitted to EJOR
dc.identifierhttps://arxiv.org/abs/math/0605498
dc.identifierhttp://arxiv.org/abs/math/0605498
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131060
dc.subjectOptimization and Control
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectNeural and Evolutionary Computing
dc.subjectRobotics
dc.subjectStatistics Theory
dc.titleCross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe
dc.typetext

Files

Collections

arXiv