{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T21:09:15Z","timestamp":1782335355098,"version":"3.54.5"},"reference-count":0,"publisher":"AI Access Foundation","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jair"],"abstract":"<jats:p>Lifelong Multi-Agent Path Finding (MAPF) is critical for modern warehouse automation, which requires multiple robots to continuously navigate conflict-free paths to optimize the overall system throughput. However, the complexity of warehouse environments and the long-term dynamics of lifelong MAPF often demand costly adaptations to classical search-based solvers. While machine learning methods have been explored, their superiority over search-based methods remains inconclusive. In this paper, we introduce Reinforcement Learning (RL) guided Rolling Horizon Prioritized Planning (RL-RH-PP), the first framework integrating RL with search-based planning for lifelong MAPF. Specifically, we leverage classical Prioritized Planning (PP) as a backbone for its simplicity and flexibility in integrating with a learning-based priority assignment policy. By formulating dynamic priority assignment as a Partially Observable Markov Decision Process (POMDP), RL-RH-PP exploits the sequential decision-making nature of lifelong planning while delegating complex spatial-temporal interactions among agents to reinforcement learning. An attention-based neural network autoregressively decodes priority orders on-the-fly, enabling efficient sequential single-agent planning by the PP planner. Evaluations in realistic warehouse simulations show that RL-RH-PP achieves the highest total throughput among baselines and generalizes effectively across agent densities, planning horizons, and warehouse layouts. Our interpretive analyses reveal that RL-RH-PP proactively prioritizes congested agents and strategically redirects agents from congestion, easing traffic flow and boosting throughput. These findings highlight the potential of learning-guided approaches to augment traditional heuristics in modern warehouse automation.<\/jats:p>","DOI":"10.1613\/jair.1.20611","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T03:13:17Z","timestamp":1774408397000},"source":"Crossref","is-referenced-by-count":1,"title":["Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation"],"prefix":"10.1613","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3768-5556","authenticated-orcid":false,"given":"Han","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6639-8547","authenticated-orcid":false,"given":"Yining","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3094-1587","authenticated-orcid":false,"given":"Brandon","family":"Araki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3528-8185","authenticated-orcid":false,"given":"Jingkai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8594-303X","authenticated-orcid":false,"given":"Cathy","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16860","published-online":{"date-parts":[[2026,3,24]]},"container-title":["Journal of Artificial Intelligence Research"],"original-title":[],"link":[{"URL":"https:\/\/www.jair.org\/index.php\/jair\/article\/download\/20611\/27283","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.jair.org\/index.php\/jair\/article\/download\/20611\/27283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T03:15:28Z","timestamp":1775013328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jair.org\/index.php\/jair\/article\/view\/20611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1613\/jair.1.20611","relation":{},"ISSN":["1076-9757"],"issn-type":[{"value":"1076-9757","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]}}}