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arXiv AI By Alex DeWeese, Jiaoyang Li, Guannan Qu

A Theoretical Framework for Parallel Lifelong MAPF Using Group Decentralized Planning

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The paper presents a theoretical analysis of the Rolling‑Horizon Collision Resolution (RHCR) framework for Lifelong Multi‑Agent Path Finding (L‑MAPF), proving its near‑optimality in a discounted MDP setting. Building on this, the authors introduce Group Decentralized RHCR (GD‑RHCR), which partitions agents via a transitive communication scheme and plans each partition in parallel, achieving similar optimality guarantees while reducing per‑plan computational cost. Experiments across various maps demonstrate that GD‑RHCR scales to higher agent counts with high throughput and lower cost compared to vanilla RHCR.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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