# Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding

arXiv: https://arxiv.org/abs/2308.11234 · HTML: https://mlsanity.com/paper/2308.11234/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding](https://mlsanity.com/paper/2010.01367/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search](https://mlsanity.com/paper/2103.07116/) (2021)
- [Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding](https://mlsanity.com/paper/2305.03632/) (2023)
- [Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers](https://mlsanity.com/paper/1912.00253/) (2019)
- [Optimizing Space Utilization for More Effective Multi-Robot Path Planning](https://mlsanity.com/paper/2109.04677/) (2021)
- [Tracking Progress in Multi-Agent Path Finding](https://mlsanity.com/paper/2305.08446/) (2023)

## Built on by (most cited first, 15 of 15)

- [Guidance Graph Optimization for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2402.01446/) (2024)
- [Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunities](https://mlsanity.com/paper/2404.16162/) (2024)
- [Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPF](https://mlsanity.com/paper/2409.14491/) (2024)
- [Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2410.21415/) (2024)
- [RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks](https://mlsanity.com/paper/2503.02992/) (2025)
- [Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems](https://mlsanity.com/paper/2510.03472/) (2025)
- [Lifelong Scalable Multi-Agent Realistic Testbed and A Comprehensive Study on Design Choices in Lifelong AGV Fleet Management Systems](https://mlsanity.com/paper/2602.15721/) (2026)
- [Optimization of Edge Directions and Weights for Mixed Guidance Graphs in Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2602.23468/) (2026)
- [Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields](https://mlsanity.com/paper/2505.22753/) (2025)
- [GRAND: Guidance, Rebalancing, and Assignment for Networked Dispatch in Multi-Agent Path Finding](https://mlsanity.com/paper/2512.03194/) (2025)
- [GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planning](https://mlsanity.com/paper/2603.04659/) (2026)
- [A Lightweight Traffic Map for Efficient Anytime LaCAM*](https://mlsanity.com/paper/2603.07891/) (2026)
- [Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination](https://mlsanity.com/paper/2607.24336/) (2026)
- [Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF](https://mlsanity.com/paper/2608.06702/) (2026)
- [Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields](https://mlsanity.com/paper/2610.18186/) (2026)

## Abstract (arXiv)

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Although many works appear on this topic, all current algorithms struggle as the number of agents grows. The principal reason is that existing approaches typically plan free-flow optimal paths, which creates congestion. To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. We evaluate the idea in two large-scale settings: one-shot MAPF, where each agent has a single destination, and lifelong MAPF, where agents are continuously assigned new destinations. Empirically, we report large improvements in solution quality for one-short MAPF and in overall throughput for lifelong MAPF.