End-to-end reinforcement learning for autonomous longitudinal control using advantage actor critic with temporal context
This is the repo for paper End-to-end reinforcement learning for autonomous longitudinal control using advantage actor critic with temporal context. Trains an A2C policy to control an autonomous vehicle in a highway, vehicle following setting.
Clone the repo
git clone https://github.com/sampo-kuutti/e2e-rl-longitudinal-control
install requirements:
pip install -r requirements.txt
For training the model, run train_a2c.py
.
If you find the code useful in your research or wish to cite it, please use the following BibTeX entry.
@inproceedings{kuutti2019end,
title={End-to-end reinforcement learning for autonomous longitudinal control using advantage actor critic with temporal context},
author={Kuutti, Sampo and Bowden, Richard and Joshi, Harita and de Temple, Robert and Fallah, Saber},
booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
pages={2456--2462},
year={2019},
organization={IEEE}
}