IALight: Importance-Aware Multi-Agent Reinforcement Learning for Arterial Traffic Cooperative Control
Multi-intersection cooperative control for arterial or network scenarios is a crucial issue in urban traffic management. Multi-agent reinforcement learning (MARL) has been recognised as an efficient solution and shows outperformed results. However, most existing MARL-based methods treat intersection...
Saved in:
Main Authors: | Lu WEI, Xiaoyan ZHANG, Lijun FAN, Lei GAO, Jian YANG |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Zagreb, Faculty of Transport and Traffic Sciences
2025-02-01
|
Series: | Promet (Zagreb) |
Subjects: | |
Online Access: | https://traffic2.fpz.hr/index.php/PROMTT/article/view/650 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control
by: Francois-Xavier Devailly, et al.
Published: (2024-01-01) -
WORKING PRINCIPLE AND PERFORMANCE EVOLUTION OF CAMERA-BASED INTELLIGENT SIGNALIZED INTERSECTIONS: SAMSUN CITY, TÜRKIYE EXAMPLE
by: Metin Mutlu AYDIN, et al.
Published: (2023-12-01) -
A cooperative jamming decision-making method based on multi-agent reinforcement learning
by: Bingchen Cai, et al.
Published: (2025-02-01) -
SMART TRAFFIC SIGNAL CONTROL SYSTEM FOR TWO INTER-DEPENDENT INTERSECTIONS IN AKURE, NIGERIA
by: AJIBESIN SAMSON, et al.
Published: (2022-10-01) -
Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control
by: Tianyu Shi, et al.
Published: (2024-01-01)