论文标题
量子多无人机增强学习的软件仿真和可视化
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
论文作者
论文摘要
量子机学习（QML）根据其轻训练参数的数量和速度受到了很多关注； QML的进步导致对量子多代理增强学习（QMARL）的积极研究。现有的经典多代理增强学习（MARL）具有非平稳性和不确定的特性。因此，本文提出了一个新型QMARL的模拟软件框架，以控制自动量的多式无人机，即量子多无人机增强学习。我们提出的框架通过更少的可训练参数来实现合理的奖励收敛和服务质量性能。此外，它显示出更稳定的训练结果。最后，我们提出的软件使我们能够分析培训过程和结果。
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.
