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Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems

Adrien Couetoux 1, 2 
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : In this thesis, I studied sequential decision making problems, with a focus on the unit commitment problem. Traditionnaly solved by dynamic programming methods, this problem is still a challenge, due to its high dimension and to the sacrifices made on the accuracy of the model to apply state of the art methods. I investigated on the applicability of Monte Carlo Tree Search methods for this problem, and other problems that are single player, stochastic and continuous sequential decision making problems. In doing so, I obtained a consistent and anytime algorithm, that can easily be combined with existing strong heuristic solvers.
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https://theses.hal.science/tel-00927252
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Submitted on : Sunday, January 12, 2014 - 11:44:14 AM
Last modification on : Sunday, June 26, 2022 - 12:00:29 PM
Long-term archiving on: : Saturday, April 8, 2017 - 2:20:56 PM

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Adrien Couetoux. Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems. Data Structures and Algorithms [cs.DS]. Université Paris Sud - Paris XI, 2013. English. ⟨NNT : ⟩. ⟨tel-00927252⟩

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