QiMARL: Quantum-Inspired Multi-Agent Reinforcement Learning Strategy for Efficient Resource Energy Distribution in Nodal Power Stations

The coupling of quantum computing with multi-agent reinforcement learning (MARL) provides an exciting direction to tackle intricate decision-making tasks in high-dimensional spaces. This work introduces a new quantum-inspired multi-agent reinforcement learning (QiMARL) model, utilizing quantum parallelism to achieve learning efficiency and scalability improvement. The QiMARL model is tested on an energy distribution task, which optimizes power distribution between generating and demanding nodal power stations. We compare the convergence time, reward performance, and scalability of QiMARL with traditional Multi-Armed Bandit (MAB) and Multi-Agent Reinforcement Learning methods, such as Greedy, Upper Confidence Bound (UCB), Thompson Sampling, MADDPG, QMIX, and PPO methods with a comprehensive ablation study. Our findings show that QiMARL yields better performance in high-dimensional systems, decreasing the number of training epochs needed for convergence while enhancing overall reward maximization. We also compare the algorithm’s computational complexity, indicating that QiMARL is more scalable to high-dimensional quantum environments. This research opens the door to future studies of quantum-enhanced reinforcement learning (RL) with potential applications to energy optimization, traffic management, and other multi-agent coordination problems.