Research on Improved Moving Edge Computing Task Unloading Algorithm Based on Deep Reinforcement Learning
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.
deep reinforcement learningedge computing task offloadingsame strategy experience replayentropy regularity
万方数据
维普
