基于强化学习的网络时延自动化控制数学建模研究

传统的网络时延控制模型在分析时延原因时,仅从宏观角度分析,缺少建立网络模型的过程,导致时延控制能力差、数据传输时间长、丢包率大的问题。为解决此问题,设计一种基于强化学习的网络时延自动化控制模型。该模型的构建主要分为两部分,先是确定网络模型,具体分析网络时延出现的原因,在此基础上,利用强化学习中的Q学习算法构建自动化控制模型,以解决网络时延问题。实验结果表明:与传统的基于均衡调度的网络时延控制模型相比,该模型对网络时延的控制性能更好,且数据包传输时间缩短3.7 s,数据包丢包率降低5%,应用优势明显。

Research on mathematical modeling of network delay automation control based on reinforcement learning

When analyzing the causes of delay,the traditional network delay control model only is analyzed from the macroscopic perspective,and it lacks the process of establishing network model,which leads to the problems of poor delay control ability,l ong data transmission time and high packet loss rate.To solve this problem,a model of network time delay automation control is designed based on reinforcement learning.The construction of this model is mainly divided into two parts.First,the network model is determined,and the causes of network delay are analyzed in detail.On this basis,the Q learning algorithm in reinforcement learning is used to build an automatic control model to solve the network delay problem.The experimental results show that compared with the traditional network delay control model based on balanced scheduling,this model has better performance in controlling network delay,and the transmission time of packet is shortened by 3.7s and packet loss rate is reduced by 5%.