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.

本文介绍了一种新型的基于量子机器学习的自主多无人机控制框架 - 量子多无人机强化学习框架，并在该框架下经过充分的训练和分析，实现了合理的回报收敛和服务质量表现，具有更稳定的训练结果及优秀的训练过程分析功能。