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1 CMAP - Centre de Mathématiques Appliquées 2 SEQUEL - Sequential Learning LIFL - Laboratoire d-Informatique Fondamentale de Lille, LAGIS - Laboratoire d-Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe

Abstract : Our setting is a Partially Observable Markov Decision Process with continuous state, observation and action spaces. Decisions are based on a Particle Filter for estimating the belief state given past observations. We consider a policy gradient approach for parameterized policy optimization. For that purpose, we investigate sensitivity analysis of the performance measure with respect to the parameters of the policy, focusing on Finite Difference FD techniques. We show that the naive FD is subject to variance explosion because of the non-smoothness of the resampling procedure. We propose a more sophisticated FD method which overcomes this problem and establish its consistency.

Keywords : Partially Observable Markov Decision Problems sensitivity analysis particle filtering parametric optimization





Autor: Pierre Arnaud Coquelin - Romain Deguest - Rémi Munos -

Fuente: https://hal.archives-ouvertes.fr/



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