GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning

no code implementations • 17 Sep 2020 • Chang Liu, Huichu Zhang, Wei-Nan Zhang, Guanjie Zheng, Yong Yu

The heavy traffic congestion problem has always been a concern for modern cities.

Clustering Generative Adversarial Network +4

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

1 code implementation • 13 May 2019 • Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Wei-Nan Zhang, Yong Yu, Haiming Jin, Zhenhui Li

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

Multi-agent Reinforcement Learning reinforcement-learning +1

Learning Phase Competition for Traffic Signal Control

1 code implementation • 12 May 2019 • Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li

Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.

Reinforcement Learning (RL)

CoLight: Learning Network-level Cooperation for Traffic Signal Control

4 code implementations • 11 May 2019 • Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Wei-Nan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li

To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.

Multi-agent Reinforcement Learning

pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data

no code implementations • 22 Oct 2016 • Julie Yixuan Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor O. K. Li, Jiawei Han, Yu Zheng

Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}.