Abstract-Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work. Index Terms-Artificial intelligence (AI), bandit-based methods, computer Go, game search, Monte Carlo tree search (MCTS), upper confidence bounds (UCB), upper confidence bounds for trees (UCT).
Monte Carlo tree search (MCTS) has proven to be a particularly powerful method to address decision processes, especially in the context of games REF .
A Survey of Monte Carlo Tree Search Methods
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games
Computer Science