Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation

TL;DR

This paper introduces a quantum multi-agent reinforcement learning algorithm designed for autonomous mobility cooperation, improving scalability, parameter efficiency, and convergence speed in multi-agent systems within the NISQ era.

Contribution

The paper proposes a novel quantum MARL algorithm with an actor-critic network and a projection value measure for enhanced scalability, efficiency, and convergence in multi-agent autonomous systems.

Findings

QMARL outperforms classical algorithms in reward and convergence speed.

PVM reduces action dimension logarithmically, improving scalability.

QMARL demonstrates superior parameter utilization and scalability.

Abstract

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.