# Tracking Progress in Multi-Agent Path Finding

arXiv: https://arxiv.org/abs/2305.08446 · HTML: https://mlsanity.com/paper/2305.08446/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 6 of 6)

- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding](https://mlsanity.com/paper/2010.01367/) (2020)
- [Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://mlsanity.com/paper/1901.11282/) (2019)
- [LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding](https://mlsanity.com/paper/2211.13432/) (2022)
- [Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search](https://mlsanity.com/paper/2103.07116/) (2021)
- [MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings](https://mlsanity.com/paper/2102.12461/) (2021)

## Built on by (most cited first, 3 of 3)

- [Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2308.11234/) (2023)
- [Engineering LaCAM^ast: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.04292/) (2023)
- [A Lightweight Traffic Map for Efficient Anytime LaCAM*](https://mlsanity.com/paper/2603.07891/) (2026)

## Abstract (arXiv)

Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many potential competitors, and the computational burden for comprehensive experimentation is prohibitively large. Moreover, detailed data from past experimentation is usually unavailable. In this work, we introduce a set of methodological and visualisation tools which can help the community establish clear indicators for state-of-the-art MAPF performance and which can facilitate large-scale comparisons between MAPF solvers. Our objectives are to lower the barrier of entry for new researchers and to further promote the study of MAPF, since progress in the area and the main challenges are made much clearer.