In the era of big data,the scale of mobile terminal users continues to expand,and the Internet of everything brings great conve-nience to people.At the same time,there is also the problem of geographic dispersion of a large amount of data,which brings great challenges to the QoS of user service.In this paper,a task unloading model based on the three-layer service architecture of the mobile edge computing platform is first built.Combined with the actual application scenario of the MEC platform,the deep reinforcement learning algorithm is im-proved by using the same policy experience playback and entropy regularization,and the task unloading strategy of the MEC platform is opti-mized.Experiments are designed to compare and analyze the three indexes of energy consumption,delay and network usage of the three tradi-tional algorithms and the improved algorithm,and verify that the improved algorithm has better performance in reducing energy consumption,delay and network usage.
关键词
深度强化学习/边缘计算任务卸载/同策略经验回放/熵正则
Key words
deep reinforcement learning/edge computing task offloading/same strategy experience replay/entropy regularity
维普
万方数据