Monte Carlo Tree Search
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About
Monte Carlo Tree Search (MCTS) is a technique for making optimal decisions in AI problems, particularly in combinatorial games. It combines random simulation with tree search precision. MCTS is notable for its success in computer Go and has many other applications beyond games, including gambling strategies and probability mathematics. The algorithm builds a search tree based on simulated outcomes and focuses on the most relevant nodes, making it well-suited for complex decision-making tasks. Although MCTS performs well in various domains, it can struggle with games of medium complexity and requires significant iterations for reliable solutions. Various enhancements for the basic algorithm have been developed to improve performance and adaptability.
Platform
Task
Features
• handles complex decision-making under uncertainty
• simple to implement
• can be halted at any time to return best estimate
• allows for adaptive tree growth focusing on relevant nodes
• utilizes upper confidence bounds (ucb) method
• applies to games, gambling, and ai problems
• combines random simulation with precision tree search
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