一种基于神经元强化学习的网络拥塞控制方法  

A CONGESTION CONTROL SCHEME BASED ON NEURON REINFORCEMENT LEARNING

作　　者：周川[1] 狄东杰[1] 陈庆伟[1] 郭毓[1] 

机构地区：[1]南京理工大学自动化学院,南京210094

出　　处：《动力学与控制学报》2011年第1期54-57,共4页Journal of Dynamics and Control

基　　金：江苏省自然科学基金(BK2007206);南京理工大学自主科研专项计划(2010GJPY066);南京市留学回国启动基金资助项目~~

摘　　要：提出了一种基于神经元强化学习(Neuron-based Reinforcement Learning,NRL)的自适应AQM算法,采用链路速率和队列长度作为拥塞指示,可根据网络环境的变化在线自动调整神经元参数,从而保持良好的队列长度稳定性和对网络负载波动的鲁棒性.该算法结构简单、易于实现,且不依赖对象的模型.仿真结果表明,该算法尤其适合于解决复杂不确定性网络的拥塞控制问题,并具有更好的队列稳定性和鲁棒性.A novel adaptive AQM scheme based on Neuron Reinforcement Learning （NRL） was presented.This scheme uses queue length and link rate as congestion notification to determine an appropriate drop/mark probability,and the parameters of neuron can be adjusted online according to the time-varying network environment,so that the stability of queue dynamics and robustness for fluctuation of TCP loads are guaranteed.This scheme is easy to implement with simple structure,and it is independent of the system model to be controlled.Simulation results show that the proposed algorithm can be effective in solving congestion control problem for complex network with uncertainties,and it has better performance on stability and robustness.

关 键 词：拥塞控制 主动队列管理(AQM) 神经元 强化学习 

分 类 号：TP393.06[自动化与计算机技术—计算机应用技术]