Using Local Regression in Monte Carlo Tree Search

Abstract

In this non-comparative study paper, local regression modeling is added to Monte Carlo Tree Search (MCTS). This local regression modeling is used to replace the random action selection in the simulation part of MCTS. The modified MCTS method and the regular MCTS method were tested against each other on the tic-tac-toe and the connect four games. The results of the experiment indicated that local regression helped the modified MCTS to outperform the regular MCTS on moderately memory demanding games, such as connect four. The performances of the two approaches appear to equal each other on low branching factor and less memory demanding games similar to tic-tac-toe.