Abstract
In recent years the Monte Carlo tree search revolution has spread from computer Go to many areas, including computer Hex. MCTS-based Hex players now outperform traditional knowledge-based alpha-beta search players, and the reigning Computer Olympiad Hex gold medallist is the MCTS player MoHex. In this paper we show how to strengthen MoHex, and observe that—as in computer Go—using learned patterns in priors and replacing a hand-crafted simulation policy by a softmax policy that uses learned patterns significantly increases playing strength. The result is MoHex 2.0, about 250 Elo points stronger than MoHex on the 11\(\times \)11 board, and 300 Elo points stronger on the 13\(\times \)13 board.
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The Elo gain from win rate \(r\) is \(400*-\log ((1/r)-1)\).
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Huang, SC., Arneson, B., Hayward, R.B., Müller, M., Pawlewicz, J. (2014). MoHex 2.0: A Pattern-Based MCTS Hex Player. In: van den Herik, H., Iida, H., Plaat, A. (eds) Computers and Games. CG 2013. Lecture Notes in Computer Science(), vol 8427. Springer, Cham. https://doi.org/10.1007/978-3-319-09165-5_6
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