@inproceedings{a7be3114f6bf4d0bb1ce79a27ceba815,
title = "Dynamic traffic signal control based on multi-agent curricular transfer learning",
abstract = "This paper considers the smart city traffic signal control problem. The application of reinforcement learning in smart city traffic signal control has always been a hot research field. However, agents cannot learn good policies in complex environments with a large number of agents. Therefore, this paper proposes a course learning method with increasing number of agents in a hybrid environment, completes multi-agent course transfer learning based on MADDPG algorithm, and applies it to the field of traffic lights in smart city. Experimental results show that the performance of the proposed system is better than the widely used traffic signal control algorithms in large-scale intersection environment.",
keywords = "Multi-agent, traffic signal control problem, transfer learning",
author = "Shangting Miao and Bin Wang and Yang Li and Quan Pan",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE. All rights reserved.; 2023 International Workshop on Signal Processing and Machine Learning, WSPML 2023 ; Conference date: 22-09-2023 Through 24-09-2023",
year = "2023",
doi = "10.1117/12.3012811",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yang Yue",
booktitle = "International Workshop on Signal Processing and Machine Learning, WSPML 2023",
}
