# Automatic Algorithm Selection In Multi-agent Pathfinding

arXiv: https://arxiv.org/abs/1906.03992 · HTML: https://mlsanity.com/paper/1906.03992/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddings](https://mlsanity.com/paper/2102.12461/) (2021)
- [Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.19219/) (2025)
- [Algorithm Selection for Optimal Multi-Agent Path Finding via Graph Embedding](https://mlsanity.com/paper/2406.10827/) (2024)
- [DeepClean: Integrated Distortion Identification and Algorithm Selection for Rectifying Image Corruptions](https://mlsanity.com/paper/2407.16302/) (2024)

## Abstract (arXiv)

In a multi-agent pathfinding (MAPF) problem, agents need to navigate from their start to their goal locations without colliding into each other. There are various MAPF algorithms, including Windowed Hierarchical Cooperative A*, Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case that there is no a single algorithm that dominates all MAPF instances. Therefore, in this paper, we investigate the use of deep learning to automatically select the best MAPF algorithm from a portfolio of algorithms for a given MAPF problem instance. Empirical results show that our automatic algorithm selection approach, which uses an off-the-shelf convolutional neural network, is able to outperform any individual MAPF algorithm in our portfolio.