用小型无人机群接力传数据，测试强化学习的扩展性。
Dynamic one-time delivery of critical data by small and sparse UAV swarms: a model problem for MARL scaling studies
- 设计确定性任务模拟无人机群传数据，用于研究MARL可扩展性。
- 少量无人机时，现有强化学习算法表现接近基准方案。
- 随着无人机增多，现有算法性能明显下降，暴露扩展瓶颈。
本文研究多智能体强化学习（MARL）在无人飞行器去中心化控制中的应用，目标是将关键数据包传递至已知位置。为此，提出一组用于MARL可扩展性研究的确定性博弈任务。设计了一种鲁棒基线策略，限制智能体运动并采用Dijkstra最短路径算法。计算实验表明，在智能体数量较少时，两种现成的MARL算法表现与基线相当；但随着智能体数量增加，这些算法出现明显的可扩展性问题。源代码和动画已公开于https://github.com/mikapersson/Information-Relaying。
原文摘要 · Abstract (English)
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.
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