Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
This addresses scalability and efficiency issues in cooperative autonomous mobility systems for Industry 4.0, though it appears incremental as it builds on existing quantum and MARL concepts.
The paper tackles the problems of huge parameter utilization and convergence difficulties in multi-agent reinforcement learning (MARL) for autonomous mobility systems by proposing a quantum MARL (QMARL) algorithm with projection value measure (PVM), achieving the highest reward through reduced action dimension and improved scalability.
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.