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MAPF solved via multi-marginal optimal transport and Schrödinger bridges

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新方法，通过将其重新表述为一种特定的多边际最优传输（MMOT）问题来解决多智能体路径查找（MAPF）问题。该方法利用马尔可夫结构将MMOT的计算复杂度降低到多项式大小的线性规划。对于大规模应用，该方法通过薛定谔桥进一步调整，薛定谔桥提供了一种迭代的、Sinkhorn类型的解决方案，在保持近乎最优结果的同时显著降低了复杂度。 AI

影响 引入了一种更高效的多机器人协调方法，可能对物流和自主系统产生影响。

排序理由 该集群包含一篇详细介绍解决复杂计算问题新方法的学术论文。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

MAPF solved via multi-marginal optimal transport and Schrödinger bridges

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报道来源 [2]

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

    通过多边际最优传输和薛定谔桥实现最优可扩展MAPF

    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 ·

    通过多边际最优传输和薛定谔桥实现最优可扩展MAPF

    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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