The task offloading technology plays a crucial vital role in the Internet of Vehicle (IoV) with the demands of delay minimum, by jointly optimizing the heterogeneous computing resources supported by the vehicles, roadside units (RSUs), and macro base stations (MBSs). In previous works, on the one hand, they ignored the wireless interference among the exchange and sharing of the task data. On the other hand, the available resources supported by the vehicles that have similar driving behaviors, which can form a vehicle platooning (VEH-PLA) and effectively integrate the resources of individual vehicle, has not been addressed. In addition, as a novel resource management paradigm, the VEH-PLA should consider the task categorization, since vehicles in VEH-PLA may have the same task offloading requests, which also has not attracted enough attention. In this paper, considering the wireless interference, mobility, VEH-PLA, and task categorization, we propose four kinds of task offloading models for the purpose of the processing delay minimum. Furthermore, by utilizing centralized training and decentralized execution (CTDE) based on multi-agent deep reinforcement learning (MADRL), we present a task offloading decision-making method to find the global optimal offloading decision, resulting in a significant enhancement in the load balancing of resources and processing delay. Finally, the simulations demonstrate that the proposed method significantly outperforms traditional task offloading methods in terms of the processing delay minimum while keeping the resource load balancing.

翻译：任务卸载技术在车联网中发挥着至关重要的作用，其目标是通过联合优化由车辆、路边单元和宏基站支持的异构计算资源，实现延迟最小化。在以往的研究中，一方面忽视了任务数据交换与共享过程中的无线干扰问题；另一方面，对于具有相似驾驶行为的车辆所形成的车辆编队（VEH-PLA）——这种能够有效整合单车资源的可用资源支持模式——尚未得到充分探讨。此外，作为一种新型资源管理范式，VEH-PLA 需要考虑任务分类问题，因为编队内车辆可能具有相同的任务卸载请求，这一方面也未受到足够重视。本文综合考虑无线干扰、移动性、VEH-PLA 及任务分类等因素，以处理延迟最小化为目标，提出了四种任务卸载模型。进一步地，通过采用基于多智能体深度强化学习的集中训练分散执行框架，我们提出了一种任务卸载决策方法，以寻找全局最优卸载决策，从而显著提升资源负载均衡与处理延迟性能。仿真实验表明，所提方法在保持资源负载均衡的同时，在处理延迟最小化方面显著优于传统任务卸载方法。