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Monte Carlo Tree Search: A Review of Recent Modifications and Applications
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Monte Carlo Tree Search: A Review of Recent Modifications and Applications
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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.
Forward citations
Cited by 3 Pith papers
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Active Perception for Radio Map Reconstruction in Uncharted 3D Air-Ground Environments
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Improve Mathematical Reasoning in Language Models by Automated Process Supervision
OmegaPRM automates collection of 1.5 million process supervision labels via binary-search MCTS, raising Gemini Pro math accuracy from 51% to 69.4% on MATH500 and Gemma2 27B from 42.3% to 58.2%.
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Search-Based Multi-Trajectory Refinement for Safe C-to-Rust Translation with Large Language Models
LAC2R uses MCTS to systematically explore multiple LLM refinement trajectories for C-to-Rust translation and reports superior safety and correctness on small-scale benchmarks.
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