强化学习
计算机科学
信号(编程语言)
控制(管理)
功能(生物学)
钢筋
国家(计算机科学)
交通信号灯
控制信号
人工智能
机器学习
实时计算
算法
工程类
程序设计语言
电信
结构工程
进化生物学
传输(电信)
生物
作者
Junxiu Liu,Sheng Qin,Min Su,Yuling Luo,Shunsheng Zhang,Yanhu Wang,Su Yang
标识
DOI:10.1016/j.eswa.2023.120458
摘要
Reinforcement Learning (RL) is an effective method for adaptive traffic signals control. As one type of RL, the teacher-student framework has been found helpful in improving the model performance for different application fields (such as robot control, game, hybrid intelligence), but it is rarely applied for traffic control due to that the hyper-parameters and the number of state-action pairs experienced are difficult to determine. In this work, the teacher-student framework is used for traffic signal control, where only a single reward function is designed to guide the student agent and by using this method the number of hyper-parameters and the model complexity are reduced. Specifically, the teacher agent uses an importance function to evaluate and guide the student, where the importance function combines with environment reward to form a synthetic reward for the student agent. Experimental results under different traffic environments show that the proposed method achieves the expected performance enhancement and is better than most of the state-of-the-art RL-based traffic signal control methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI