Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drones, i.e., quantum multi-drone reinforcement learning. Our proposed framework accomplishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.

翻译：量子机器学习（QML）因其训练参数数量少、速度快而备受关注，其发展推动了量子多智能体强化学习（QMARL）的积极研究。经典的多智能体强化学习（MARL）存在非平稳性和不确定性特征。因此，本文提出一种新颖的QMARL仿真软件框架，用于控制自主多无人机系统，即量子多无人机强化学习。本框架以较少的可训练参数实现了合理的奖励收敛性与服务质量性能，同时展现出更稳定的训练结果。最后，所提出的软件支持对训练过程及结果进行分析。