GPLight

Genetic Programming as An Alternative to Reinforcement Learning for Traffic Signal Control

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This is a example code for paper "Learning Traffic Signal Control via Genetic Programming ".

The testing platform for the algorithm is CityFlow and GPLight is adapted as an intelligent agent in LibSignal.

Usage

This project does not have complex third-party dependencies!

Download

Please execute the following command to install and configure our environment.

git clone https://github.com/Rabbytr/gplight.git

If you don't have Git on your computer, you can download the zip file directly.

Requirements

You can use pip to directly install the following dependencies.

pip install -r requirements.txt

deap==1.4.1
numpy==2.0.0
libsumo==1.20.0
gym==0.26.2
pyyaml==6.0.1
pathos==0.3.2

To install CityFlow simulator:

To ensure that CityFlow installs successfully, please make sure that CMake is installed on your computer and C++ can be compiled on your computer

git clone https://github.com/cityflow-project/CityFlow.git
cd CityFlow
pip install -e .

The version of pybind11 in source code of CityFlow is unfortunately fixed. If you encounter installation errors, you may consider upgrading the pybind11 version included in CityFlow.

Run GPLight

After installing the dependencies mentioned above, you can directly run GPLight.

python run_gplight.py

To quickly get feedback on running and keep you from getting bored 😂, the parameters set in run_gplight.py are relatively small. Once the code runs correctly, please set appropriate parameters or those recommended in the paper.

Citation 🙏

If you encounter any difficulty using our code, please do not hesitate to submit an issue or directly contact us! If you find our work helpful (or if you are so kind as to offer us some encouragement), please consider giving us a star, and citing our paper.

@misc{liao2024learningtrafficsignalcontrol,
      title={Learning Traffic Signal Control via Genetic Programming}, 
      author={Xiao-Cheng Liao and Yi Mei and Mengjie Zhang},
      year={2024},
      eprint={2403.17328},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2403.17328}, 
}