Awesome Monte Carlo Tree Search
A curated collection of Monte Carlo tree search research papers with implementations from top AI conferences.
What is Awesome Monte Carlo Tree Search?
Awesome Monte Carlo Tree Search Papers is a curated GitHub repository that collects academic research papers on Monte Carlo Tree Search (MCTS) along with their code implementations. It addresses the problem of fragmented access to cutting-edge MCTS research by providing a centralized, organized list of papers from top-tier AI conferences and journals. The repository helps researchers and engineers quickly discover relevant work and available code for applications ranging from game playing to automated theorem proving.
AI researchers, graduate students, and machine learning engineers who are exploring or applying Monte Carlo Tree Search algorithms in areas like reinforcement learning, robotics, automated reasoning, or game theory. It is particularly valuable for those seeking reproducible research with accessible implementations.
Developers choose this repository because it saves significant time in literature review by aggregating high-quality MCTS papers with code links in one place. Its unique value lies in the combination of rigorous academic curation (focusing on top conferences) and practical utility (including implementations), which is not typically found in standard academic databases or generic paper lists.
Overview
A curated list of Monte Carlo tree search papers with implementations.
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