Heterogeneous Information Fusion in Graph-Based Asynchronous Hierarchical Multi-Agent Reinforcement Learning for Hybrid-Action Task Offloading

Mobile edge computing (MEC) has emerged as an effective solution for processing compute-intensive and delay-sensitive tasks in multi-user, multi-server environments. To better orchestrate such complex and heterogeneous scenarios, we integrate a software-defined network to enable centralized coordination of computation offloading and resource allocation. Based on this architecture, a time-variant mixed-integer optimization problem is formulated to minimize system-wide latency and energy cost. Multi-agent reinforcement learning (MARL) offers a theoretical framework for solving this problem, yet its application introduces two major challenges. First, partial observability arises because user equipment (UEs) lack access to global information, which hinders coordinated decision-making. Second, the system involves a hybrid action space comprising discrete offloading decisions and continuous bandwidth allocations, complicating the policy learning process. To address these issues, we propose a novel graph-based asynchronous hierarchical MARL (GAH-MARL) algorithm that enables multi-agent and multi-level information fusion. A graph-communication module allows UEs to selectively share local observations, thereby mitigating partial observability. In addition, a hierarchical learning structure integrates information across decision levels and decouples the optimization of discrete and continuous actions. Furthermore, asynchronous training is incorporated to reduce computational cost and enhance learning efficiency. Simulation results demonstrate that GAH-MARL significantly outperforms existing MARL baselines in reducing latency and energy consumption, particularly under large-scale deployments.