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)因其轻度培训参数数和速度而得到很多关注;QML的进步导致对量子多剂强化学习(QMARL)进行积极研究。现有的经典多剂强化学习(MARL)具有非常态性和不确定性质。因此,本文件为新型QMARL提供了一个模拟软件框架,以控制自主的多底盘,即量子多质强化学习。我们提议的框架实现了合理的奖励趋同和服务质量业绩,而培训参数较少。此外,它显示了更稳定的培训结果。最后,我们提议的软件使我们能够分析培训过程和结果。