IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control

We've also investigated traffic signal control problems in multi-intersection control scenarios. If you are interested, please refer to PressLight and CoLight. For a more comphrehensive view on RL-based traffic signal control, you can visit this project homepage.

Introduction

This project has been published as the following conference paper:

Hua Wei*, Guanjie Zheng*, Huaxiu Yao, Zhenhui Li, IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control, in Proceedings of the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18), London, UK, August 2018.

(*Co-First author.)

The full paper and demo can be found at the author's website.

This project proposes a reinforcement learning based intelligent traffic light control system. Simply run the runexp.py to run the experiment. Please change the parameters in conf/ folder and runexp.py correspondingly if needed. Also, please specify the location of TraCI module in map_computor.py if necessary.

Before running above codes, you may need to install following packages or environments:

Python 3.6
SUMO 0.32 with TraCI module. Please specify TraCI location correpsondingly in map_computor.py
Keras 2.2.0 and Tensorflow 1.9.0

Files in the folder

The files are functioning like this:

runexp.py

Load the experiment setting, and call traffic_light_agent.
traffic_light_dqn.py

Simulate the reinforcement learning agent. This file simulates the timeline, get current state from sumo_agent, get current state from sumo_agent and call the agent to make decision.
sumo_agent.py

Interact with map_computor to get state, reward from the map_computor, and convey action to map_computor.
map_computor.py

Read data from SUMO and operate SUMO.
agent.py

Abstract class of agent.
network_agent.py

Abstract class of neural network based agent.
deeplight_agent.py

Class of our method.
conf/

Configuration files.
data/

Traffic flow files. The intersection is connected with four road segments of 300-meters long.

If you are new to SUMO, hope these slides can help you with a good start (http://personal.psu.edu/hzw77/posts.html#sumo-introduction).

Citation

@inproceedings{wei2018intellilight,
  title={IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control},
  author={Wei, Hua and Zheng, Guanjie and Yao, Huaxiu and Li, Zhenhui},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2496--2505},
  year={2018},
  organization={ACM}
}

wyn430/IntelliLight

IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control