This work studies the application of Multi-Agent Reinforcement Learning (MARL) to decentralized control of unmanned aerial vehicles to relay a critical data package to a known position. For this purpose, a family of deterministic games is introduced, designed for MARL scaling studies. A robust baseline policy is proposed which restricts agent motion and applies Dijkstra's shortest path algorithm. Computational experiment results show that two off-the-shelf MARL algorithms perform competitively with the baseline for a small number of agents, but face scalability issues as the number of agents increases. Source code and animations are available online at https://github.com/mikapersson/Information-Relaying.
@article{arxiv.2512.09682,
title = {Dynamic one-time delivery of critical data by small and sparse UAV swarms: a model problem for MARL scaling studies},
author = {Mika Persson and Jonas Lidman and Jacob Ljungberg and Samuel Sandelius and Adam Andersson},
journal= {arXiv preprint arXiv:2512.09682},
year = {2026}
}