科技创新与应用2024，Vol.14Issue(10)：55-58,4.DOI:10.19981/j.CN23-1581/G3.2024.10.014
基于强化学习的网络拥塞控制算法
摘要
Abstract
In this paper,based on reinforcement learning,a QLCC algorithm is proposed,which describes the network congestion process as a Markov decision process and innovatively designs a new network congestion control algorithm based on the application of Q-learning algorithm.In the course of the research,the reinforcement learning method is first introduced,and the construction conditions and assumptions of Markov decision process in the process of network congestion are discussed,and then the QLCC algorithm is introduced from the aspects of frame structure,parameter structure and definition,parameter discrete partition and update steps.However,simulation experiments are used to test the network throughput,fairness and throughput of the new algorithm in random packet loss environment.By comparison and analysis with other three traditional network congestion control algorithms,it is proved that QLCC algorithm has better throughput,highest fairness and best anti-packet loss performance,indicating that it is an intelligent network congestion control algorithm with high application advantages.关键词
强化学习/QLCC算法/网络拥塞控制/学习方法/仿真实验Key words
reinforcement learning/QLCC algorithm/network congestion control/learning method/simulation experiment分类
信息技术与安全科学引用本文复制引用
李凡,李慧斯,马文丹..基于强化学习的网络拥塞控制算法[J].科技创新与应用,2024,14(10):55-58,4.基金项目
广州软件学院科研项目(ky202122,ky202123) （ky202122,ky202123）
