Quantum computing (QC) has received a lot of attention according to its light
training parameter numbers and computational speeds by qubits. Moreover,
various researchers have tried to enable quantum machine learning (QML) using
QC, where there are also multifarious efforts to use QC to implement quantum
multi-agent reinforcement learning (QMARL). Existing classical multi-agent
reinforcement learning (MARL) using neural network features non-stationarity
and uncertain properties due to its large number of parameters. Therefore, this
paper presents a visual simulation software framework for a novel QMARL
algorithm to control autonomous multi-drone systems to take advantage of QC.
Our proposed QMARL framework accomplishes reasonable reward convergence and
service quality performance with fewer trainable parameters than the classical
MARL. Furthermore, QMARL shows more stable training results than existing MARL
algorithms. Lastly, our proposed visual simulation software allows us to
analyze the agents' training process and results.Comment: 5 pages, 4 figure