Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

Multi-Agent Path Finding (MAPF) is an NP-hard problem that has been well studied in artificial intelligence and robotics. Recently, randomized MAPF solvers have been shown to exhibit heavy-tailed distributions of runtimes, which can be exploited to boost their success rate for a given runtime limit. In this paper, we discuss different ways of randomizing MAPF solvers and evaluate simple rapid randomized restart strategies for state-of-the-art MAPF solvers such as iECBS, M* with highways and CBS-CL.

Authors

• Liron Cohen 0002
• Glenn Wagner
• David M. Chan
• Howie Choset
• Nathan R. Sturtevant
• Sven Koenig
• T. K. Satish Kumar

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