GAN and Multi-Agent DRL Based Decentralized Traffic Light Signal Control

Wang, Zixin; Zhu, Hanyu; He, Mingcheng; Zhou, Yong; Luo, Xiliang; Zhang, Ning

doi:10.1109/tvt.2021.3134329

Cited by 18 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shi et al [20] proposed an efficient and robust data augmenta-tion method using GAN-generated samples. Sandra Treneska et al [23] first focused on image colorization with GAN because of their ability to generate the most realistic colorization results. Then, via transfer learning, they used this as a proxy task for visual understanding.…”

Section: Data Augmentation Methodsmentioning

confidence: 99%

A Risk Prediction Model Based on Crash History Data for Railway Trams

Ji,

Yang,

Wang

et al. 2023

Chinese J. Elect.

View full text Add to dashboard Cite

Risk prediction is an important task to ensuring the driving safety of railway trams. Although data-driven intelligent methods are proved to be effective for driving risk prediction, accuracy is still a top concern for the challenges of data quality which mainly represent as the unbalanced datasets. This study focuses on applying feature extraction and data augmentation methods to achieve effective risk prediction for railway trams, and proposes an approach based on a self-adaptive K-means clustering algorithm and the least squares deep convolution generative adversarial network (LS-DCGAN). The data preprocessing methods are proposed, which include the K-means algorithm to cluster the locations of trams and the extreme gradient boosting recursive feature elimination based feature selection algorithm to retain the key features. The LS-DCGAN model is designed for sparse sample expansion, aiming to address the sample category distribution imbalance problem. The experiments implemented with the public and real datasets show that the proposed approach can reach a high accuracy of 90.69%, which can greatly enhances the tram driving safety.

show abstract

Section: Data Augmentation Methodsmentioning

confidence: 99%

A Risk Prediction Model Based on Crash History Data for Railway Trams

Ji,

Yang,

Wang

et al. 2023

Chinese J. Elect.

View full text Add to dashboard Cite

show abstract

“…Zhou et al ( 23 ) introduces an edge computing enabled approach that utilizes Internet of vehicle (IoV) data in a efficient way. On the other hand, Wang et al ( 24 ) enriches decentralized agents’ observation with a generative adversarial network to complement the global state.…”

Section: Related Workmentioning

confidence: 99%

eMARLIN: Distributed Coordinated Adaptive Traffic Signal Control with Topology-Embedding Propagation

Wang,

Taitler,

Smirnov

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

In this paper, we examine the practical problem of minimizing the delay in traffic networks that are controlled at each intersection independently, without a centralized supervisory computer and with limited communication bandwidth. We find that existing learning algorithms have lackluster performance or are too computationally complex to be implemented in the field. Instead, we introduce a simple yet efficient and effective approach using multi-agent reinforcement learning (MARL) that applies the Deep Q-Network (DQN) learning algorithm in a fully decentralized setting. First, we decouple the DQN into per-intersection Q-networks and then transmit the output of each Q-network’s hidden layer to its intersection neighbors. We show that our method is computationally efficient compared with other MARL methods, with minimal additional overhead compared with a naive isolated learning approach with no communication. This property enables our method to be implemented in real-world scenarios with less computation power. Finally, we conduct experiments for both synthetic and real-world scenarios and show that our method achieves better performance in minimizing intersection delay than other methods.

show abstract

“…Traffic congestion in cities is problematic as it causes pollution, financial loss and increases the risk of accidents [51]. Hence, controlling the traffic through traffic signals, line controls or routing guidance has great potential to improve living conditions.…”

Section: A Traffic Controlmentioning

confidence: 99%

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

M.¹,

Brosig²,

Plinge³

et al. 2022

Preprint

View full text Add to dashboard Cite

Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that only utilize single, independent agents leave potential performance gains on the road. Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other. This work aims to give an overview of the field to researchers in autonomous mobility. We first explain MARL and introduce important concepts. Then, we discuss the central paradigms that underlie MARL algorithms, and give an overview of state-of-the-art methods and ideas in each paradigm. With this background, we survey applications of MARL in autonomous mobility scenarios and give an overview of existing scenarios and implementations.

show abstract

GAN and Multi-Agent DRL Based Decentralized Traffic Light Signal Control

Cited by 18 publications

References 36 publications

A Risk Prediction Model Based on Crash History Data for Railway Trams

A Risk Prediction Model Based on Crash History Data for Railway Trams

eMARLIN: Distributed Coordinated Adaptive Traffic Signal Control with Topology-Embedding Propagation

An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility

Contact Info

Product

Resources

About