Improvements to MCTS Simulation Policies in GoPublic
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Since its introduction in 2006, Monte-Carlo Tree Search has been a major breakthrough in computer Go. Performance of an MCTS engine is highly dependent on the quality of its simulations, though despite this, simulations remain one of the most poorly understand aspects of MCTS. In this paper, we explore in-depth the simulations policy of Pachi, an open-source computer Go agent. This research attempts to better understand how simulation policies affect the overall performance of MCTS, building on prior work in the field by doing so. Through this research we develop a deeper understanding of the underlying components in Pachi's simulation policy, which are common to many modern MCTS Go engines, and evaluate the metrics used to measure them.
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