MultiTree MCTS in Tabletop Games

We introduce MultiTree Monte Carlo Tree Search (MT-MCTS), in which a tree is constructed independently for each player. This permits deeper search for the acting agent’s own move, at the cost of a poorer opponent model and the loss of conditioning a move on the specific action of another player.We test MT-MCTS in eleven different tabletop board and card games, with varying numbers of players. The main benefit occurs in simultaneous-move games, where independent trees better model the information structure. We find that in other games MT-MCTS can outperform vanilla MCTS, which incorporates all players in a single tree, but that this advantage usually decreases as the computational budget increases, and the cost of poor opponent modelling outweighs the gain from deeper search.