Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
On Pruning Techniques for Multi-Player Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
CG '08 Proceedings of the 6th international conference on Computers and Games
Monte-Carlo Tree Search Solver
CG '08 Proceedings of the 6th international conference on Computers and Games
An Analysis of UCT in Multi-player Games
CG '08 Proceedings of the 6th international conference on Computers and Games
CG '08 Proceedings of the 6th international conference on Computers and Games
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
Evaluation function based monte-carlo LOA
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
The relative history heuristic
CG'04 Proceedings of the 4th international conference on Computers and Games
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Monte-Carlo Tree Search (MCTS) is becoming increasingly popular for playing multi-player games. In this paper we propose two enhancements for MCTS in multi-player games: (1) Progressive History and (2) Multi-Player Monte-Carlo Tree Search Solver (MP-MCTS-Solver). We analyze the performance of these enhancements in two different multi-player games: Focus and Chinese Checkers. Based on the experimental results we conclude that Progressive History is a considerable improvement in both games and MP-MCTS-Solver, using the standard update rule, is a genuine improvement in Focus.