Optimal Policies Search for Sensor Management

Thomas Bréhard 1 Emmanuel Duflos 1, 2 Philippe Vanheeghe 1, 2 Pierre-Arnaud Coquelin 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line (in simulation ) using models of the environement and of the sensor(s). The learned policy can then be used to manage the sensor(s). In order to approximate the gradient in a stochastic context, we introduce a new method to approximate the gradient, based on Infinitesimal Perturbation Approximation (IPA). The effectiveness of this general framework is illustrated by the managing of an Electronically Scanned Array Radar. First simulations results are finally proposed.
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  • HAL Id : inria-00368875, version 1
  • ARXIV : 0903.3329

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Thomas Bréhard, Emmanuel Duflos, Philippe Vanheeghe, Pierre-Arnaud Coquelin. Optimal Policies Search for Sensor Management. FUSION 2008, Jun 2008, Cologne, Germany. pp.1 - 8. ⟨inria-00368875⟩

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