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A Survey of Monte Carlo Tree Search Methods

Cameron Browne, Edward Powley, Daniel Whitehouse, Simon Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, Simon Colton

Journal Article
IEEE Transactions on Computational Intelligence and AI in Games
Volume 4
Issue 1
pp.1–43
March, 2012
IEEE
DOI 10.1109/TCIAIG.2012.2186810
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 algorithms derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

Notes

IEEE copy: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6145622

Draft copy: http://www.mcts.ai/pubs/mcts-survey-master.pdf

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