Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning
This work addresses challenges in controlling autonomous multi-drones using quantum-enhanced methods, representing an incremental advancement in applying quantum machine learning to multi-agent systems.
The paper tackled the problem of non-stationarity and uncertainty in classical multi-agent reinforcement learning by developing a simulation software framework for quantum multi-drone reinforcement learning, achieving reasonable reward convergence and service quality with fewer trainable parameters and more stable training results.
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.