Paper Info
Title
GAN and Multi-Agent DRL Based Decentralized Traffic Light Signal Control
Abstract
Adaptive traffic light signal control (ATSC) is a promising paradigm for alleviating traffic congestion in intelligent transportation systems. Most of the existing methods require heavy traffic data exchange among neighboring intersections to achieve collaborative ATSC, which may not be supported by bandwidth-limited communication links in practice. In this article, we develop a communication-efficient decentralized ATSC framework for traffic networks with multiple intersections, where each intersection only exchanges traffic statistics with its neighboring intersections. In particular, the proposed framework consists of a generative adversarial network (GAN) based algorithm for traffic data recovery, and a multi-agent deep reinforcement learning (DRL) based decentralized ATSC algorithm for traffic efficiency enhancement. By adopting the value decomposition technique that establishes a nonlinear mapping from the local state-action values to the global reward, each intersection can independently determine its traffic light signal based on its local traffic data while achieving collaboration among neighboring intersections. Our proposed decentralized ATSC framework is scalable to large-scale traffic networks, and is also robust to traffic flow variations via interacting with the environment. Simulations show that our proposed algorithm can significantly reduce the vehicle travel time while maintaining high and stable traffic throughput.
Year
DOI
Venue
2022
10.1109/TVT.2021.3134329
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Adaptive traffic light signal control,multi-agent deep reinforcement learning,generative adversarial network
Journal
71
Issue
ISSN
Citations 
2
0018-9545
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Zixin Wang111.70
Hanyu Zhu233.45
Mingcheng He321.42
Yong Zhou4325.61
Xiliang Luo551.09
Ning Zhang6276.14