详情

原文传递 Multiagent Soft Actor-Critic for Traffic Light Timing

题名：	Multiagent Soft Actor-Critic for Traffic Light Timing
正文语种：	eng
作者：	Lan Wu;Yuanming Wu;Cong Qiao;Yafang Tian
作者单位：	Dept. of Electrical Engineering Henan Univ. of Technology Zhengzhou 450001 PR China;Dept. of Electrical Engineering Henan Univ. of Technology Zhengzhou 450001 PR China;Dept. of International Education Zhengzhou Railway Vocational & Technical College Zhengzhou 451460 PR China;Dept. of International Education Zhengzhou Railway Vocational & Technical College Zhengzhou 451460 PR China
关键词：	Traffic light timing; Multi-agent Soft Actor-Critic (SAC); Deep reinforcement learning; Convergence and divergence; Exploration capabilities; Entropy item
摘要：	Deep reinforcement learning has strong perception and decision-making capabilities that can effectively solve the problem of continuous high-dimensional state-action space and has become the mainstream method in the field of traffic light timing. However, due to model structural defects or different strategic mechanisms of models, most deep reinforcement learning models have problems such as convergence and divergence or poor exploration capabilities. Therefore, this paper proposes a multi-agent Soft Actor-Critic (SAC) for traffic light timing. Multi-agent SAC adds an entropy item to measure the randomness of the strategy in the objective function of traditional reinforcement learning and maximizes the sum of expected reward and entropy item to improve the model's exploration ability. The system model can learn multiple optimal timing schemes, avoid repeated selection of the same optimal timing scheme and fall into a local optimum or fail to converge. Meanwhile, it abandons low reward value strategies to reduce data storage and sampling complexity, accelerate training, and improve the stability of the system. Comparative experiments show that the method based on multi-agent SAC traffic light timing can solve the existing problems of deep reinforcement learning and improve the efficiency of vehicles passing through in different traffic scenarios.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	2
页码：	04022133.1-04022133.11