Quantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control
Soohyun Park, Gyu Seon Kim, Sovi Jung +1 more
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-drone mobility, i.e., quantum multi-drone reinforcement learning. Our proposed framework ac-complishes 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.
IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)•Aug 21, 2024•2 citations