Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Bandit-based optimization on graphs with application to library performance tuning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Creating an upper-confidence-tree program for havannah
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Investigating monte-carlo methods on the weak schur problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Monte Carlo Tree Search is a recent algorithm that achieves more and more successes in various domains. We propose an improvement of the Monte Carlo part of the algorithm by modifying the simulations depending on the context. The modification is based on a reward function learned on a tiling of the space of Monte Carlo simulations. The tiling is done by regrouping the Monte Carlo simulations where two moves have been selected by one player. We show that it is very efficient by experimenting on the game of Havannah.