QMARL: A Quantum Multi-Agent Reinforcement Learning Framework for Swarm Robots Navigation
Weizhao Chen, Jiawang Wan, Fangwen Ye, Ran Wang, Cheng Xu · 2024
In the last decade, the field of reinforcement learning has evolved from single-agent paradigms to embrace multi-agent settings. However, as the number of agents increases, especially in intricate or stochastic environments, the efficacy of individual learning models tends to diminish. Moreover, applying experience replay techniques in multi-agent scenarios presents considerable challenges. To address these pressing issues, this paper introduces a straightforward yet highly effective approach known as Quantum-Based Multi-Agent Reinforcement Learning (QMARL). This approach revolves around the quantization of states and actions within the multi-agent reinforcement learning system. Leveraging the power of the Grover algorithm for action decision-making, we also introduce a novel quantum-based prioritized experience replay method. Our proposed approach has been rigorously validated through experiments conducted in the cooperative navigation environment provided by OpenAI. The results demonstrate its capacity to enhance multi-agent learning in complex settings. This research opens promising avenues for harnessing quantum computing techniques in the realm of reinforcement learning, paving the way for more robust and scalable solutions in multi-agent systems.