Monte-Carlo tree search for multi-player, no-limit Texas hold’em poker

摘要

This talk presents a number of adaptations that allow the application of Monte-Carlo Tree Search (MCTS) to the field of computer Poker, more specifically No-Limit Texas Hold’em. The hidden information in Poker results in so called miximax game trees where opponent decision nodes have to be modeled as chance nodes. The probability distribution in these nodes is modeled by an opponent model that predicts the actions of the opponents. We propose a modification of the standard MCTS selection and backpropagation strategies that explicitly model and exploit the uncertainty of sampled expected values. The new strategies are evaluated as a part of a complete Poker bot that is, to the best of our knowledge, the first exploiting no-limit Texas Hold’em bot that can play at a reasonable level in games of more than two players.