Research on deep reinforcement learning in Internet of vehicles edge computing based on Quasi-Newton method - ZHANG Jianwu; LU Zetao; ZHANG Qianhua; ZHAN Ming (Tongxin xuebao)
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) algorithm 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 enhanced under conditions of multitasking and dynamic wireless channels, improving computational efficiency. By introducing 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 algorithm. 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.