Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme.

翻译：车载边缘计算（VEC）是一种新兴技术，它使车辆能够通过本地执行任务或将任务卸载到附近的边缘设备来完成高强度的计算任务。然而，建筑物等障碍物可能会降低通信质量并导致通信中断，从而使车辆可能无法满足任务卸载的要求。可重构智能表面（RIS）被引入以支持车辆通信并提供替代的通信路径。通过灵活调整RIS的相移，可以改善系统性能。针对任务随机到达的RIS辅助VEC系统，我们设计了一种综合考虑卸载功率、本地功率分配和相移优化的控制方案。为解决这一非凸优化问题，我们提出了一种新的深度强化学习（DRL）框架，该框架采用改进的多智能体深度确定性策略梯度（MADDPG）方法来优化车辆用户（VU）的功率分配，并采用块坐标下降（BCD）算法来优化RIS的相移。仿真结果表明，我们提出的方案优于集中式深度确定性策略梯度（DDPG）方案和随机方案。