Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

Authors

  • Chacha Chen Pennsylvania State University
  • Hua Wei Pennsylvania State University
  • Nan Xu University of Southern California
  • Guanjie Zheng Pennsylvania State University
  • Ming Yang
  • Yuanhao Xiong Zhejiang University
  • Kai Xu Shanghai Tianrang Intelligent Technology Co., Ltd
  • Zhenhui Li Pennsylvania State University

DOI:

https://doi.org/10.1609/aaai.v34i04.5744

Abstract

Traffic congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for traffic signal control. However, the primary challenge is to control and coordinate traffic lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand traffic lights. In this paper, we tackle the problem of multi-intersection traffic signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difficult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 traffic lights in Manhattan, New York City 12.

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Published

2020-04-03

How to Cite

Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., Xu, K., & Li, Z. (2020). Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3414-3421. https://doi.org/10.1609/aaai.v34i04.5744

Issue

Section

AAAI Technical Track: Machine Learning