# Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

arXiv: https://arxiv.org/abs/2005.07371 · HTML: https://mlsanity.com/paper/2005.07371/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 30 of 80)

- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers](https://mlsanity.com/paper/2212.11498/) (2022)
- [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023)
- [Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search](https://mlsanity.com/paper/2103.07116/) (2021)
- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [Multi-Robot Coordination and Layout Design for Automated Warehousing](https://mlsanity.com/paper/2305.06436/) (2023)
- [MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale](https://mlsanity.com/paper/2409.00134/) (2024)
- [Safe Learning of Lifted Action Models](https://mlsanity.com/paper/2107.04169/) (2021)
- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [Improving Learnt Local MAPF Policies with Heuristic Search](https://mlsanity.com/paper/2403.20300/) (2024)
- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [Multi-Goal Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2208.01223/) (2022)
- [Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding](https://mlsanity.com/paper/2312.15908/) (2023)
- [Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities](https://mlsanity.com/paper/2404.16162/) (2024)
- [Arbitrarily Scalable Environment Generators via Neural Cellular Automata](https://mlsanity.com/paper/2310.18622/) (2023)
- [Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Search](https://mlsanity.com/paper/2412.13359/) (2024)
- [SRMT: Shared Memory for Multi-agent Lifelong Pathfinding](https://mlsanity.com/paper/2501.13200/) (2025)
- [Graph-Based Multi-Robot Path Finding and Planning](https://mlsanity.com/paper/2206.11319/) (2022)
- [Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding](https://mlsanity.com/paper/2403.07559/) (2024)
- [Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2410.21415/) (2024)
- [SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding](https://mlsanity.com/paper/2307.02691/) (2023)
- [RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks](https://mlsanity.com/paper/2503.02992/) (2025)
- [Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding](https://mlsanity.com/paper/2208.01222/) (2022)
- [Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction](https://mlsanity.com/paper/2411.01707/) (2024)
- [Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning](https://mlsanity.com/paper/2506.23793/) (2025)
- [Standby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks](https://mlsanity.com/paper/2201.06014/) (2022)
- [Exact Algorithms and Lowerbounds for Multiagent Pathfinding: Power of Treelike Topology](https://mlsanity.com/paper/2312.09646/) (2023)
- [Improved Anonymous Multi-Agent Path Finding Algorithm](https://mlsanity.com/paper/2312.10572/) (2023)
- [SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding](https://mlsanity.com/paper/2502.06440/) (2025)
- [Lightweight and Effective Preference Construction in PIBT for Large-Scale Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.12623/) (2025)

## Abstract (arXiv)

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a bounded time horizon and ignores collisions beyond it. RHCR is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work.