Point-based Incremental Pruning for Monte-Carlo Tree Search

Monte-Carlo tree search (MCTS) combines the generality of stochastic simulation and the accuracy of tree search, which has attracted the great attention of scholars. However, the MCTS search requires a sufficient number of iterations to converge to a good solution, which is more difficult to optimize. In order to solve this problem, this paper presents a point-based incremental pruning (PIP) for Monte-Carlo tree search. Instead of reasoning about the whole policy trees space when pruning the cross-sums of the value functions during policy construction, our algorithm uses boundary belief points to perform exact pruning, and exploits intermediate points to perform approximate pruning by generating policy trees, then uses real-time belief states to get the optimal policy in policy execution. The theoretical analysis and empirical results indicate that PIP can speed up the tree search efficiently.