基于拟牛顿法的深度强化学习在车联网边缘计算中的研究OA北大核心CSTPCD

To address the issues of ineffective task offloading decisions caused by multitasking and resource constraints in vehicular networks,the Quasi-Newton method deep reinforcement learning dual-phase online offloading(QNRLO)al-gorithm was proposed.The algorithm was designed by initially incorporating batch normalization techniques to optimize the training process of deep neural networks.Subsequently,optimization was performed using the Quasi-Newton method to effectively approximate the optimal solution.Through this dual-stage optimization,performance was significantly en-hanced under conditions of multitasking and dynamic wireless channels,improving computational efficiency.By intro-ducing Lagrange multipliers and a reconstructed dual function,the non-convex optimization problem was transformed into a convex optimization problem of the dual function,ensuring the global optimality of the algorithm.Additionally,system transmission time allocation in the vehicular network model was considered,enhancing the practicality of the al-gorithm.Compared to existing algorithms,the proposed algorithm improves the convergence and stability of task offloading significantly,addresses task offloading issues in vehicular networks effectively,and offers high practicality and reliability.