Graph-Augmented MAPPO: Dynamic Topology-Aware Task Offloading Optimization in Edge Computing's data

In multi-access edge computing (MEC) environments, joint optimization of task offloading and resource allocation is challenging due to dynamic topologies, limited resources, and network heterogeneity. This letter proposes a Graph-Augmented Multi-Agent Proximal Policy Optimization (GA-MAPPO) framework under a centralized training and decentralized execution (CTDE) paradigm. A Master\u2013Agent cooperative architecture integrates a Graph Attention Network (GAT) to model dynamic connectivity and resource variations. The method employs a hybrid action space for unified optimization of offloading and resource allocation. Extensive simulations with real-world mobility and workload traces show that GA-MAPPO outperforms representative baselines in task completion rate, energy efficiency, latency, and training stability, demonstrating its effectiveness and practical potential in dynamic MEC and Internet of Vehicles (IoV) scenarios.