# Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding arXiv: https://arxiv.org/abs/2410.21415 ยท HTML: https://mlsanity.com/paper/2410.21415/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. - Builds on 24 of its 33 references that are in the map. - First posted on arXiv 28 Oct 2024. ## Placed in - Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/) ## Builds on (most cited first, 24 of 24) - [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021) - [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019) - [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018) - [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020) - [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017) - [PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong](https://mlsanity.com/paper/2010.08184/) (2020) - [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019) - [Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planning](https://mlsanity.com/paper/2011.13219/) (2020) - [Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning](https://mlsanity.com/paper/2005.05420/) (2020) - [SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2303.00605/) (2023) - [Distributed Heuristic Multi-Agent Path Finding with Communication](https://mlsanity.com/paper/2106.11365/) (2021) - [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021) - [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023) - [MAPPER: Multi-Agent Path Planning with Evolutionary Reinforcement Learning in Mixed Dynamic Environments](https://mlsanity.com/paper/2007.15724/) (2020) - [Multi-Agent Path Finding with Prioritized Communication Learning](https://mlsanity.com/paper/2202.03634/) (2022) - [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) - [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) - [A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph](https://mlsanity.com/paper/2010.05254/) (2020) - [SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding](https://mlsanity.com/paper/2307.02691/) (2023) - [Receding Horizon Re-ordering of Multi-Agent Execution Schedules](https://mlsanity.com/paper/2312.04190/) (2023) - [RDE: A Hybrid Policy Framework for Multi-Agent Path Finding Problem](https://mlsanity.com/paper/2311.01728/) (2023) ## Built on by (most cited first, 7 of 7) - [DeepFleet: Multi-Agent Foundation Models for Mobile Robots](https://mlsanity.com/paper/2508.08574/) (2025) - [Learning-guided Prioritized Planning for Lifelong Multi-Agent Path Finding in Warehouse Automation](https://mlsanity.com/paper/2603.23838/) (2026) - [Planning over MAPF Agent Dependencies via Multi-Dependency PIBT](https://mlsanity.com/paper/2603.23405/) (2026) - [MAPF-World: Action World Model for Multi-Agent Path Finding](https://mlsanity.com/paper/2508.12087/) (2025) - [Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF](https://mlsanity.com/paper/2608.06702/) (2026) - [Decentralized Master-Mind: Joint Action Refinement through Iterative Intent Denoising in Multi-Agent Pathfinding](https://mlsanity.com/paper/2609.32019/) (2026) - [Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields](https://mlsanity.com/paper/2610.18186/) (2026) ## Abstract (arXiv) Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local observations. However, it is still challenging for them to match the performance of the best search-based algorithms, especially in large-scale settings. This work proposes an imitation-learning-based LMAPF solver that introduces a novel communication module as well as systematic single-step collision resolution and global guidance techniques. Our proposed solver, Scalable Imitation Learning for LMAPF (SILLM), inherits the fast reasoning speed of learning-based methods and the high solution quality of search-based methods with the help of modern GPUs. Across six large-scale maps with up to 10,000 agents and varying obstacle structures, SILLM surpasses the best learning- and search-based baselines, achieving average throughput improvements of 137.7% and 16.0%, respectively. Furthermore, SILLM also beats the winning solution of the 2023 League of Robot Runners, an international LMAPF competition. Finally, we validated SILLM with 10 real robots and 100 virtual robots in a mock warehouse environment.