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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
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 : 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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Submitted on : Monday, December 3, 2012 - 4:50:42 AM
Last modification on : Sunday, June 26, 2022 - 11:57:31 AM
Long-term archiving on: : Monday, March 4, 2013 - 3:44:37 AM


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  • HAL Id : hal-00759822, version 1



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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