QQ客服 官方微博 反馈留言

	REINFORCEMENT LEARNING FOR RAMP CONTROL: AN ANALYSIS OF LEARNING PARAMETERS
	Lu C.; Huang, J.; Gong, J. W.
	2016
关键词	reinforcement learning Q-learning ramp control agent macroscopic traffic flow model freeway model
英文摘要	Reinforcement Learning (RL) has been proposed to deal with ramp control problems under dynamic traffic conditions; however, there is a lack of sufficient research on the behaviour and impacts of different learning parameters. This paper describes a ramp control agent based on the RL mechanism and thoroughly analyzed the influence of three learning parameters; namely, learning rate, discount rate and action selection parameter on the algorithm performance. Two indices for the learning speed and convergence stability were used to measure the algorithm performance, based on which a series of simulation-based experiments were designed and conducted by using a macroscopic traffic flow model. Simulation results showed that, compared with the discount rate, the learning rate and action selection parameter made more remarkable impacts on the algorithm performance. Based on the analysis, some suggestions about how to select suitable parameter values that can achieve a superior performance were provided.
出处	Promet-Traffic & Transportation
卷	28
期	4
页	371-381
收录类别	SCI
语种	英语
ISSN号	0353-5320
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/43273]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Lu C.,Huang, J.,Gong, J. W.. REINFORCEMENT LEARNING FOR RAMP CONTROL: AN ANALYSIS OF LEARNING PARAMETERS. 2016.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。