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MAPF solved via multi-marginal optimal transport and Schrödinger bridges

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel approach to solve multi-agent path finding (MAPF) problems by reformulating them as a specific type of multi-marginal optimal transport (MMOT) problem. This method leverages a Markovian structure to reduce the computational complexity of MMOT to a polynomial-sized linear program. For large-scale applications, the approach is further adapted using Schrödinger bridges, which provide an iterative, Sinkhorn-type solution that significantly reduces complexity while maintaining near-optimal results. AI

IMPACT Introduces a more efficient method for multi-robot coordination, potentially impacting logistics and autonomous systems.

RANK_REASON The cluster contains an academic paper detailing a new method for solving a complex computational problem.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MAPF solved via multi-marginal optimal transport and Schrödinger bridges

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The cluster contains an academic paper detailing a new method for solving a complex computational problem.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schrödinger Bridges

    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 stru…

  2. arXiv cs.LG TIER_1 English(EN) · Joseph W. Durham ·

    Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schrödinger Bridges

    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 stru…

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