For Industry 4.0 Revolution, cooperative autonomous mobility systems are
widely used based on multi-agent reinforcement learning (MARL). However, the
MARL-based algorithms suffer from huge parameter utilization and convergence
difficulties with many agents. To tackle these problems, a quantum MARL (QMARL)
algorithm based on the concept of actor-critic network is proposed, which is
beneficial in terms of scalability, to deal with the limitations in the noisy
intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also
beneficial in terms of efficient parameter utilization and fast convergence due
to quantum supremacy. Note that the reward in our QMARL is defined as task
precision over computation time in multiple agents, thus, multi-agent
cooperation can be realized. For further improvement, an additional technique
for scalability is proposed, which is called projection value measure (PVM).
Based on PVM, our proposed QMARL can achieve the highest reward, by reducing
the action dimension into a logarithmic-scale. Finally, we can conclude that
our proposed QMARL with PVM outperforms the other algorithms in terms of
efficient parameter utilization, fast convergence, and scalability.

基于量子供应链，并结合多智能体强化学习和量子优势，提出了一种能够实现多智能体合作、有效利用参数和快速收敛的量子 MARL 算法，并引入了投影值测量技术来进一步提高可伸缩性。