Source code for paper "GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog Computing Network".
- SUMO >= 1.19.
- Python >= 3.11 (older versions are not tested).
- Clone or download this repository.
- Install dependencies.
pip install -r requirements.txt
The model can be trained using train.py. The user will need to specify the path to a .sumocfg file using the --sumocfg argument. The program will use the corresponding .net.xml file to build the environment that the RL agent will interact with.
Use python train.py --help to see all accepted arguments that can be used to control the environment, RL model, and training.
After a model is trained, the test.py script can be used to evaluate the model. The testing simulation scenario should use the same network used for training. Use python test.py --help to see all accepted arguments.
For more technical details, please refer to the paper.
@INPROCEEDINGS{tang2024greenlight,
author={Tang, Chengyu and Baskiyar, Sanjeev},
booktitle={2024 IEEE 15th International Green and Sustainable Computing Conference (IGSC)},
title={GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog Computing Network},
year={2024},
pages={129-134},
doi={10.1109/IGSC64514.2024.00032}
}