Naveed, Munir, Kitchin, Diane E., Crampton, Andrew, Chrpa, Lukáš and Gregory, Peter (2012) A Monte-Carlo Path Planner for Dynamic and Partially Observable Environments. In: Computational Intelligence and Games (CIG), 2012 IEEE Conference on. IEEE Computational Intelligence Society, pp. 211-218. ISBN 9781467311939

In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put on fast action selection motivating the use of Monte-Carlo techniques in MOCART-CGA. Exploration of the space is guided by using corridors which direct simulations in the neighbourhood of the best found moves.

MOCART-CGA limits how many times a particular state-action pair is explored to balance exploration of the neighbourhood of the state and exploitation of promising actions. MOCART-CGA is evaluated using four standard pathfinding benchmark maps, and over 1000 instances. The empirical results show that MOCART-CGA outperforms existing techniques, in terms of search time, in dynamic and partially observable environments. Experiments have also been performed in static (and partially observable) environments where MOCART-CGA still requires less time to search than its competitors, but typically finds lower quality plans.

MOCART.pdf - Accepted Version

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