关键词: 车辆边缘计算, 多智能体强化学习, 轨迹预测, 任务卸载, 协同任务调度

Abstract: In vehicular edge computing (VEC) environments, the high mobility and uneven distribution of vehicles often result in cross-domain task offloading failures and load imbalance among roadside units (RSUs), thereby degrading overall system performance. Existing approaches primarily emphasize either load balancing or single-step offloading decisions, while insufficiently addressing communication uncertainties caused by vehicle mobility and the requirement for flexible task scheduling across RSUs, which limits offloading efficiency. To overcome these limitations, a trajectory-aware multi-agent reinforcement learning–based cooperative offloading method is introduced. A PatchTST-based multi-step vehicle trajectory prediction module is integrated to provide foresighted mobility awareness for task scheduling. A vehicle–edge–cloud collaborative offloading framework is further developed, in which vehicles and RSUs are modeled as agent clusters. A centralized training and decentralized execution (CTDE) paradigm enable joint decision-making, and a partially reward-decoupled multi-agent policy optimization mechanism is designed to improve cooperation efficiency and system stability. Extensive simulations under varying task densities, delay constraints, and network topologies show that the proposed method consistently outperforms representative baselines such as IPPO, MADDPG, and MAPPO, achieving an average improvement of 36.1% in task completion rate and a reduction of 37.6% in task completion delay. These results demonstrate the effectiveness, robustness, and practical potential of the proposed approach in dynamic and complex VEC environments.

Key words: Vehicular Edge Computing, Multi-Agent Reinforcement Learning(MARL), trajectory prediction, task offloading, collaborative task scheduling