An Enhanced MADDPG–A2C Framework for Optimized Resource Allocation in High-Speed Vehicular Networks

To address the degradation in communication performance caused by the high mobility and dynamic uncertainty in vehicular network channels, this paper proposes a hybrid resource allocation framework that integrates the advantage actor–critic (A2C) algorithm with the multi-agent deep deterministic policy gradient (MADDPG) algorithm. By modeling the high-speed vehicular network environment, the resource allocation task is formulated as a multi-agent deep reinforcement learning (MADRL) problem within a continuous action space. The proposed framework leverages the advantage function to refine gradient estimation, thereby improving training stability and convergence behavior. Additionally, regularization penalty terms and constraint mechanisms are incorporated into the learning process to balance multiple communication objectives. Specifically, the method aims to maximize the throughput of vehicle-to-infrastructure (V2I) links while ensuring the transmission reliability of vehicle-to-vehicle (V2V) links. In simulation experiments, the proposed method performs better in terms of convergence. Compared with the conventional MADDPG algorithm, the average access success probability is improved by 1.6%, and the average V2I throughput increases by 3.5%, indicating a significant enhancement in overall vehicular communication efficiency and transmission performance.