Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model. Therefore, with the objective of minimizing the AoI of V2I links and prioritizing transmission of V2V links payload, we construct this optimization problem as an Markov decision process (MDP) problem in which the BS serves as an agent to allocate resources and control phase-shift for the vehicles using the soft actor-critic (SAC) algorithm, which gradually converges and maintains a high stability. A AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme based on SAC algorithm is proposed and simulation results show that its convergence speed, cumulative reward, AoI performance, and payload transmission probability outperforms those of proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3) and stochastic algorithms.

翻译：可重构智能表面（RIS）作为通信领域的关键技术，通过提供替代路径显著提升无线通信环境中的链路质量。本文提出一种RIS辅助的车联网（IoV）网络架构，并考虑车联万物（V2X）通信模式。为提升车-基础设施（V2I）链路的时效性与车-车（V2V）链路的稳定性，我们引入信息年龄（AoI）模型与有效载荷传输概率模型。在此基础上，以最小化V2I链路AoI并优先保障V2V链路有效载荷传输为目标，将该优化问题构建为马尔可夫决策过程（MDP）问题：基站作为智能体，采用软演员-评论家（SAC）算法为车辆分配资源并控制相位偏移，该算法能逐步收敛且保持高度稳定性。本文提出基于SAC算法的AoI感知联合车载资源分配与RIS相位偏移控制方案，仿真结果表明，其在收敛速度、累积奖励、AoI性能及有效载荷传输概率方面均优于近端策略优化（PPO）、深度确定性策略梯度（DDPG）、双延迟深度确定性策略梯度（TD3）及随机算法。