Abstract:

In Vehicular Edge Computing （VEC） environments， the high mobility and uneven distribution of vehicles often lead to cross-domain task offloading failures and load imbalance of RoadSide Units （RSUs）， thereby degrading system performance significantly. The existing studies mainly focus on load balance or single-step offloading decisions， while failing to address the communication uncertainties caused by vehicle mobility and the requirement for flexible task scheduling across RSUs， which limits offloading efficiency. Therefore， a Trajectory-Aware Collaborative Multi-Agent Reinforcement Learning （TAC-MARL） method was proposed. First， foresighted mobility awareness for task scheduling was realized through introducing a Patch Time Series Transformer （PatchTST） -based multi-step vehicle trajectory prediction module to guide task scheduling. Second， a vehicle-edge-cloud collaborative task offloading framework was constructed， in which vehicles and RSUs were modeled as agent groups， cooperative decision-making was performed by the employment of a reinforcement learning paradigm — Centralized Training and Decentralized Execution （CTDE）， and a partially reward-decoupled multi-agent policy optimization method was designed to improve cooperation efficiency and system stability. Simulations under varying task densities， latency constraints， and network topologies were conducted. The proposed method consistently outperforms baseline algorithms such as Independent Proximal Policy Optimization （IPPO）， Multi-Agent Deep Deterministic Policy Gradient （MADDPG）， and Multi-Agent Proximal Policy Optimization （MAPPO） in task completion rate and task completion latency， verifying the efficiency， robustness， and practical potential of the proposed method in dynamic and complex VEC environments.

Key words: Vehicular Edge Computing (VEC), multi-agent reinforcement learning, trajectory prediction, task offloading, collaborative task scheduling

摘要：

在车辆边缘计算（VEC）环境中，车辆的高速移动性和非均匀分布常导致跨域任务卸载失败和路侧单元（RSU）负载失衡，从而严重影响系统性能。现有研究多集中于负载均衡或仅考虑单步卸载决策，忽略了车辆移动性带来的通信不确定性以及任务在跨RSU环境下的灵活调度需求，导致卸载效率受限。因此，提出一种轨迹感知协同多智能体强化学习（TAC-MARL）方法。首先，引入基于PatchTST（Patch Time Series Transformer）的多步车辆轨迹预测模块，实现前瞻性的移动感知，从而指导任务调度；其次，构建车-边-云协同任务卸载框架，将车辆与RSU建模为智能体群体，采用集中训练分散执行（CTDE）的强化学习范式进行联合决策，并设计部分奖励解耦的多智能体策略优化方法以提升协作效率与系统稳定性。在不同任务密度、时延约束及网络拓扑的仿真测试结果表明，相较于独立近端策略优化（IPPO）、多智能体深度确定性策略梯度（MADDPG）及多智能体近端策略优化（MAPPO）等先进基线算法，所提方法的任务完成率和任务完成时延显著更优，验证了该方法在动态复杂VEC环境下的高效性、鲁棒性与应用潜力。

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