# Rapid Randomized Restarts for Multi-Agent Path Finding Solvers

## TL;DR

This paper introduces rapid randomized restart strategies to improve the performance of MAPF solvers on hard instances by leveraging heavy-tailed runtime distributions and multiple short runs.

## Contribution

It proposes simple RRR strategies that enhance existing MAPF solvers by exploiting randomized restarts, leading to better performance on challenging problems.

## Key findings

- RRR strategies significantly improve solver success rates
- Heavy-tailed runtime distributions justify multiple short runs
- Performance gains validated on state-of-the-art MAPF solvers

## Abstract

Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics. It has many real-world applications for which existing MAPF solvers use various heuristics. However, these solvers are deterministic and perform poorly on "hard" instances typically characterized by many agents interfering with each other in a small region. In this paper, we enhance MAPF solvers with randomization and observe that they exhibit heavy-tailed distributions of runtimes on hard instances. This leads us to develop simple rapid randomized restart (RRR) strategies with the intuition that, given a hard instance, multiple short runs have a better chance of solving it compared to one long run. We validate this intuition through experiments and show that our RRR strategies indeed boost the performance of state-of-the-art MAPF solvers such as iECBS and M*.