Determinization in Monte-Carlo Tree Search for the card game

Monte-Carlo Tree Search (MCTS) is a class of game tree search algorithms that have recently proven successful for deterministic games of perfect information, particularly the game of Go. Determinization is an AI technique for making decisions in stochastic games of imperfect information by analysing several instances of the equivalent deterministic game of perfect information. In this paper we combine determinization techniques with MCTS for the popular Chinese card game Dou Di Zhu. In determinized MCTS, there is a trade-off between the number of determinizations searched and the time spent searching each one; however, we show that this trade-off does not significantly affect the performance of determinized MCTS, as long as both quantities are sufficiently large. We also show that the ability to see opponents’ hidden cards in Dou Di Zhu is a significant advantage, which suggests that inference techniques could potentially lead to much stronger play.