Multi-Agent Reinforcement Learning for WEEE Recycling Vehicle Path Planning Based on Graph Attention Networks

With the global surge in WEEE generation, optimizing vehicle routing for recycling has become a critical area of research. This paper introduces a MARL method built upon graph attention networks (GAT) to solve the vehicle routing problem in WEEE recycling. By representing recycling sites as graph nodes and vehicles as agents, GAT captures the intricate relationships between them. Coupled with a MARL framework, this approach enhances task efficiency and routing flexibility. Experimental results indicate that the proposed method outperforms static solvers by adapting more effectively to dynamic environments and handling demand fluctuations and complex traffic conditions. Furthermore, it demonstrates greater flexibility and collaboration compared to single-agent reinforcement learning, providing a superior solution for dynamic recycling tasks. This study lays the foundation for further exploration of MARL-GNN integration, with potential applications in waste management and sustainable logistics in smart cities.