Abstract
In this paper we investigate the use of Monte Carlo Tree Search (MCTS) on the Physical Travelling Salesman Problem (PTSP), a real-time game where the player navigates a ship across a map full of obstacles in order to visit a series of waypoints as quickly as possible. In particular, we assess the algorithm's ability to plan ahead and subsequently solve the two major constituents of the PTSP: the order of waypoints (long-term planning) and driving the ship (short-term planning). We show that MCTS can provide better results when these problems are treated separately: the optimal order of cities is found using Branch & Bound and the ship is navigated to collect the waypoints using MCTS. We also demonstrate that the physics of the PTSP game impose a challenge regarding the optimal order of cities and propose a solution that obtains better results than following the TSP route of minimum Euclidean distance.
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