Enhancements in Monte Carlo tree search algorithms for biased game trees

Monte Carlo tree search (MCTS) algorithms have been applied to various domains and achieved remarkable success. However, it is relatively unclear what game properties enhance or degrade the performance of MCTS, while the largeness of search space including pruning efficiency mainly governs the performance of classical minimax search, assuming a decent evaluation function is given. Existing research has shown that the distribution of suboptimal moves and the non-uniformity of tree shape are more important than the largeness of state space in discussing the performance of MCTS. Our study showed that another property, bias in suboptimal moves, is also important, and we present an enhancement to better handle such situations. We focus on a game tree in which the game-theoretical value is even, while suboptimal moves for a player tend to contain more inferior moves than those for the opponent. We conducted experiments on a standard incremental tree model with various MCTS algorithms based on UCB1, KL-UCB, or Thompson sampling. The results showed that the bias in suboptimal moves degraded the performance of all algorithms and that our enhancement alleviated the effect caused by this property.