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.

翻译：本研究探讨了多智能体强化学习在无人机分布式控制中的应用，旨在将关键数据包中继至已知位置。为此，我们引入了一类专为MARL扩展研究设计的确定性博弈，并提出了一种限制智能体运动并采用Dijkstra最短路径算法的鲁棒基线策略。计算实验结果表明，两种现成MARL算法在小规模智能体场景下与基线性能相当，但随着智能体数量增加面临扩展性问题。源代码及演示动画可在 https://github.com/mikapersson/Information-Relaying 获取。