Deep reinforcement learning-based joint optimization model for vehicular task offloading and resource allocation

With the rapid advancement of Internet of vehicles and autonomous driving technology, there is a growing need for increased computing power in vehicle operations. However, the strict latency requirements of vehicle tasks may pose challenges to communication and computing resources within the vehicle edge computing network. This paper introduces a two-stage joint optimization to address challenges, focusing on minimizing vehicle task latency and optimizing resource allocation. In addition, the task completion rate is considered an important indicator to ensure safety and reliability in practical application scenarios. Next, we propose a global adaptive offloading and resource allocation optimization model named GOAL. The GOAL model dynamically adjusts the weight coefficients of the reward function to optimize the model, integrating the actor-critic algorithm to effectively adapt to uncertain environments. Through experimental comparisons of various weight coefficients for task arrival rates and reward functions, we were able to determine the optimal hyperparameters for our proposed model. The simulation results show that the GOAL model outperforms the benchmark methods by over 30% in reward value. It also performs better in terms of task delay and energy consumption. Additionally, the GOAL model has a higher task completion rate compared to the benchmark methods, and it exhibits strong search capabilities and faster convergence speed.