By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge.

翻译：通过将车辆的计算密集型任务卸载至路侧单元（RSUs），车联网（IoV）中的移动边缘计算（MEC）可减轻车载计算负担。然而，现有基于模型的任务卸载方法随车辆数量增加而面临高计算复杂度问题，而数据驱动方法则缺乏可解释性。为应对这些挑战，本文提出一种知识驱动的多智能体强化学习（KMARL）方法，以降低数字孪生使能车联网中任务卸载的时延。具体而言，在所考虑的场景中，数字孪生作为每辆车的通信代理，在虚拟空间中交换信息并制定卸载决策。为降低任务卸载时延，本文提出的KMARL方法为每辆车选择最优卸载方案，其中通过利用关于图结构通信拓扑与置换不变性的领域知识，将图神经网络嵌入神经网络中。数值结果表明，得益于领域知识的集成，我们提出的KMARL方法相比其他方法获得了更高的奖励，并展现出更优的可扩展性。