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Monte-Carlo Swarm Policy Search

Abstract : Finding optimal controllers of stochastic systems is a particularly challenging problem tackled by the optimal control and reinforcement learning communities. A classic paradigm for handling such problems is provided by Markov Decision Processes. However, the resulting underlying optimization problem is difficult to solve. In this paper, we explore the possible use of Particle Swarm Optimization to learn optimal controllers and show through some non-trivial experiments that it is a particularly promising lead.
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https://hal-supelec.archives-ouvertes.fr/hal-00695540
Contributor : Sébastien van Luchene <>
Submitted on : Tuesday, June 5, 2012 - 8:31:43 AM
Last modification on : Tuesday, April 27, 2021 - 10:28:04 AM
Long-term archiving on: : Thursday, December 15, 2016 - 4:40:27 AM

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Jérémy Fix, Matthieu Geist. Monte-Carlo Swarm Policy Search. Symposium on Swarm Intelligence and Differential Evolution, Apr 2012, Zakopane, Poland. pp.75-83, ⟨10.1007/978-3-642-29353-5_9⟩. ⟨hal-00695540⟩

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