Dynamic traffic signal control based on multi-agent curricular transfer learning

Shangting Miao; Bin Wang; Yang Li; Quan Pan

doi:10.1117/12.3012811

8 December 2023 Dynamic traffic signal control based on multi-agent curricular transfer learning

Shangting Miao, Bin Wang, Yang Li, Quan Pan

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294303 (2023) https://doi.org/10.1117/12.3012811
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shangting Miao, Bin Wang, Yang Li, and Quan Pan "Dynamic traffic signal control based on multi-agent curricular transfer learning", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294303 (8 December 2023); https://doi.org/10.1117/12.3012811

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available