Hybrid Attention-Enhanced DDPG for Dynamic Task Scheduling in Vehicular Edge Computing Networks

Mobile edge computing (MEC) utilizes reinforcement learning (RL) algorithms to optimize task scheduling, reducing latency and improving efficiency. Nevertheless, conventional RL approaches are constrained to single-action spaces and fail to adapt to dynamic user demands in vehicular edge computing networks (VECNs). To address these limitations, we develop a hybrid decision-making edge-assisted vehicular computation offloading (EVCO) model, which enables adaptive latency-energy balance, thereby enhancing quality of service (QoS). Specifically, we formulate a multi-objective optimization problem to minimize vehicular user costs by jointly optimizing latency and energy consumption. Furthermore, we propose the hybrid attention-enhanced deep deterministic policy gradient (HAE-DDPG) algorithm, which efficiently handles hybrid action spaces through a dual-layer network while dynamically adjusting optimization objectives via an attention mechanism. Field experiments demonstrate that HAE-DDPG enhances vehicular user cost optimization by 5-12% compared to baselines, while maintaining adaptability to dynamic environments.