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arXiv · 1906.03992

Automatic Algorithm Selection In Multi-agent Pathfinding

Devon Sigurdson·Vadim Bulitko·Sven Koenig·Carlos Hernandez·William Yeoh
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Abstract

In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case that there is no a single algorithm that dominates all MAPF instances. Therefore, in this paper, we investigate the use of deep learning to automatically select the best MAPF algorithm from a portfolio of algorithms for a given MAPF problem instance. Empirical results show that our automatic algorithm selection approach, which uses an off-the-shelf convolutional neural network, is able to outperform any individual MAPF algorithm in our portfolio.

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Devon Sigurdson, Vadim Bulitko, Sven Koenig, Carlos Hernandez, William Yeoh. 2019-06-15. Automatic Algorithm Selection In Multi-agent Pathfinding. https://arxiv.org/abs/1906.03992

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