Dynamique d'apprentissage pour Monte Carlo Tree Search : applications aux jeux de Go et du Clobber solitaire impartial

Dynamique d'apprentissage pour Monte Carlo Tree Search : applications aux jeux de Go et du Clobber solitaire impartial

Depuis son introduction pour le jeu de Go, Monte Carlo Tree Search (MCTS) a été appliqué avec succès à d'autres jeux et a ouvert la voie à une famille de nouvelles méthodes comme Mutilple-MCTS ou Nested Monte Carlo. MCTS évalue un ensemble de situations de jeu à partir de milliers de fins de pa...

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Bibliographic Details
Main Author:	Fabbri, André
Other Authors:	Lyon 1
Language:	fr
Published:	2015
Subjects:	Intelligence artificielle pour les jeux Monte Carlo Tree Search Apprentissage par renforcement Jeux combinatoires Computer-Go Clobber Solitaire Impartial Artificial Intelligence on games Reinforcement learning Combinatorial games Impartial Solitaire Clobber 006.3
Online Access:	http://www.theses.fr/2015LYO10183/document

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