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.
@article{arxiv.2211.15375,
title = {Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning},
author = {Chanyoung Park and Jae Pyoung Kim and Won Joon Yun and Soohyun Park and Soyi Jung and Joongheon Kim},
journal= {arXiv preprint arXiv:2211.15375},
year = {2023}
}