Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Bandit-based optimization on graphs with application to library performance tuning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms
Algorithmica - Including a Special Section on Genetic and Evolutionary Computation; Guest Editors: Benjamin Doerr, Frank Neumann and Ingo Wegener
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Adding expert knowledge and exploration in monte-carlo tree search
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Creating an upper-confidence-tree program for havannah
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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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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.