Monte Carlo Tree Search (MCTS)
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About
Monte Carlo Tree Search (MCTS) is a research hub providing information and resources for MCTS, a method for making optimal decisions in AI problems, particularly move planning in combinatorial games. It combines random simulation with tree search and has applications beyond games, theoretically applicable to any domain describable in terms of {state, action} pairs and simulation. MCTS optimizes strategies for casino games like blackjack and poker, develops betting strategies for sports events, and simulates complex systems for probability estimation. It offers advantages over traditional tree search methods by not requiring strategic or tactical knowledge and performing asymmetric tree growth.
Platform
Task
Features
• asymmetric tree growth that adapts to the search space topology
• simulation of complex systems for probability estimation
• development of betting strategies for sports events
• optimization of strategies for casino games like blackjack and poker
• application in games and other domains describable in terms of {state, action} pairs and simulation
• combines random simulation with tree search
• move planning in combinatorial games
• optimal decision making in ai problems
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