IntelliLight: A Reinforcement Learning Approach For Intelligent Traffic Light Control

Authors:
Hua Wei The Pennsylvania State University
Guanjie Zheng The Pennsylvania State University
Huaxiu Yao The Pennsylvania State University
Zhenhui Li The Pennsylvania State University

Introduction:

This paper studies The intelligent traffic light control. In this paper, the authors propose a more effective deep reinforcement learning model for traffic light control.

Abstract:

The intelligent traffic light control is critical for an efficient transportation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data.

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