AI RESEARCH
Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schr\"odinger Bridges
arXiv CS.LG
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ArXi:2605.10917v1 Announce Type: new We consider anonymous multi-agent path finding (MAPF) where a set of robots is tasked to travel to a set of targets on a finite, connected graph. We show that MAPF can be cast as a special class of multi-marginal optimal transport (MMOT) problems with an underlying Markovian structure, under which the exponentially large MMOT collapses to a linear program (LP) polynomial in size.