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Conference papers

Biasing Monte-Carlo Simulations through RAVE Values

Arpad Rimmel 1 Fabien Teytaud 2, 3 Olivier Teytaud 2, 3
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 : The Monte-Carlo Tree Search algorithm has been successfully applied in various domains. However, its performance heavily depends on the Monte-Carlo part. In this paper, we propose a generic way of improving the Monte-Carlo simulations by using RAVE values, which already strongly improved the tree part of the algorithm. We prove the generality and efficiency of our approach by showing improvements on two different applications: the game of Havannah and the game of Go.
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Submitted on : Friday, May 21, 2010 - 8:44:07 AM
Last modification on : Thursday, July 8, 2021 - 3:47:59 AM
Long-term archiving on: : Thursday, September 16, 2010 - 3:05:43 PM


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  • HAL Id : inria-00485555, version 1



Arpad Rimmel, Fabien Teytaud, Olivier Teytaud. Biasing Monte-Carlo Simulations through RAVE Values. The International Conference on Computers and Games 2010, Sep 2010, Kanazawa, Japan. ⟨inria-00485555⟩



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