We release a trained RL agent from the CoRL-2017 paper "CARLA: An Open Urban Driving Simulator". This is only the inference code, the training code is not released yet.
The agent was trained with the asynchronous advantage actor-critic (A3C) algorithm by V. Mnih et al. (2016). We build on this open-source Chainer implementation: https://github.com/muupan/async-rl .
Tested with:
In Anaconda, you can create and activate an environment with installed dependencies (except for CARLA) by running:
conda create -n carla_rl python=3.6 chainer=1.24.0 cached-property=1.4.2 pillow=5.1.0 opencv=3.3.1 h5py=2.7.1
source activate carla_rl
./CarlaUE4.sh /Game/Maps/TownXX -carla-server -benchmark -fps=10 -windowed -ResX=800 -ResY=600 -carla-world-port=PORT
export PYTHONPATH=/path/to/CARLA/PythonClient:$PYTHONPATH
python run_RL.py --city-name TownXX --port PORT --corl-2017
The results will be stored in _benchmarks_results.
If you use this code in your research, please cite our CoRL 2017 paper:
@inproceedings{Dosovitskiy17,
title = { {CARLA}: {An} Open Urban Driving Simulator},
author = {Alexey Dosovitskiy and German Ros and Felipe Codevilla and Antonio Lopez and Vladlen Koltun},
booktitle = {Proceedings of the 1st Annual Conference on Robot Learning},
pages = {1--16},
year = {2017}
}