Monte-Carlo Planning for Pathfinding in Real-Time Strategy Games

In this work, we explore two Monte-Carlo planning approaches: Upper Confidence Tree (UCT) and Rapidlyexploring Random Tree (RRT). These Monte-Carlo planning approaches are applied in a real-time strategy game for solving the path finding problem. The planners are evaluated using a grid-based representation of our game world. The results show that the UCT planner solves the path planning problem with significantly less search effort than the RRT planner. The game playing performance of each planner is evaluated using the mean, maximum and minimum scores in the test games. With respect to the mean scores, the RRT planner shows better performance than the UCT planner. The RRT planner achieves more maximum scores than the UCT planner in the test games.