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Learning a Move-Generator for Upper Con dence Trees

Adrien Couetoux 1 Olivier Teytaud 1, 2 Hassen Doghmen 2
2 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : We experiment the introduction of machine learning tools to improve Monte-Carlo Tree Search. More precisely, we propose the use of Direct Policy Search, a classical reinforcement learning paradigm, to learn the Monte-Carlo Move Generator. We experiment our algorithm on di erent forms of unit commitment problems, including experiments on a problem with both macrolevel and microlevel decisions.
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https://hal.inria.fr/hal-00759822
Contributor : Adrien Couetoux <>
Submitted on : Monday, December 3, 2012 - 4:50:42 AM
Last modification on : Thursday, June 17, 2021 - 3:47:26 AM
Long-term archiving on: : Monday, March 4, 2013 - 3:44:37 AM

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Adrien Couetoux, Olivier Teytaud, Hassen Doghmen. Learning a Move-Generator for Upper Con dence Trees. International Computer Symposium 2012, Dec 2012, Hualien, Taiwan. ⟨hal-00759822⟩

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