Metraipolis › archive › dc_22faabdc4952

Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations — arXiv:2608.05588

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05588
License (as archived): CC0-1.0
Archived: 2026-10-25T20:52:19.849Z
SHA-256 of the archived text: 3f3a99b949e34b88aadd2126735fc93603a9828acbde5b09b7895c28df58190e
Stable URL of this page: https://metraipolis.com/documento/dc_22faabdc4952

How to cite

Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations — arXiv:2608.05588 — arXiv (en), https://arxiv.org/abs/2608.05588 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_22faabdc4952 (Metraipolis archive dc_22faabdc4952, sha256 3f3a99b949e34b88aadd2126735fc93603a9828acbde5b09b7895c28df58190e)

BibTeX

@misc{dc22faabdc4952,
  title = {Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations — arXiv:2608.05588},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05588},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_22faabdc4952; sha256 3f3a99b949e34b88aadd2126735fc93603a9828acbde5b09b7895c28df58190e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05588. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones. While many learning-based planners have been proposed for LMAPF, most rely on oversimplified kinematic assumptions that may overlook motion constraints critical to real-world performance. In this work, we study a more realistic LMAPF model derived from many real-world automated warehouse systems, termed LMAPF-R2, which incorporates robust safety constraints and in-place rotation constraints. These constraints substantially increase coordination difficulty, particularly in highly constrained spaces. To address these challenges, we propose Search-Aided Joint Reinforcement Learning (SJRL). We first augment neural policies with Causal PIBT, a single-step search-based planner that resolves agents' collisions and propagates their intentions. We then introduce a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search. Experiments demonstrate that SJRL achieves significant improvements over the strong search-based planner, Causal-PIBT, across multiple high-density maps. We further validate SJRL in a challenging mixed-reality warehouse environment with 8 physical robots and 248 virtual robots.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-07 in cs.MA).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2608.05588 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-25T20:47:44.142Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).