A collection of environments for autonomous driving and tactical decision-making tasks. This Library was forked from https://github.com/eleurent/highway-env and modified for our specific interests.
To use the library in Anaconda use the comand line conda env create -f av_env.yml to create a new conda environment with the prerequisites for the project.
An episode of one of the environments available in highway-env.
env = gym.make("highway-v0")In this task, the ego-vehicle is driving on a multilane highway populated with other vehicles. The agent's objective is to reach a high speed while avoiding collisions with neighbouring vehicles. Driving on the right side of the road is also rewarded.
The highway-v0 environment.
A faster variant, highway-fast-v0 is also available, with a degraded simulation accuracy to improve speed for large-scale training.
env = gym.make("roundabout-v0")In this task, the ego-vehicle if approaching a roundabout with flowing traffic. It will follow its planned route automatically, but has to handle lane changes and longitudinal control to pass the roundabout as fast as possible while avoiding collisions.
The roundabout-v0 environment.
env = gym.make("intersection-v0")An intersection negotiation task with dense traffic.
The intersection-v0 environment.
First download the repo, and open the folder highway-env
Then create the conda environment:
conda env create -f av_env.yml
This will create a conda environment with all the required libraries.
Activate conda environment:
conda activate av_env
Register Roundabout environment in Gym:
python highway_env/envs/roundabout_env.py
For train the model:
python Training_ReinforcementLearning.py
For test the model:
python ReinforcementLearning.py
Read the official documentation online.
If you use the project in your work, please consider citing the original repo with:
@misc{highway-env,
author = {Leurent, Edouard},
title = {An Environment for Autonomous Driving Decision-Making},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/eleurent/highway-env}},
}