(ICCV 2021, Oral) RL and distillation in CARLA using a factorized world model
Learning to drive from a world on rails
Dian Chen, Vladlen Koltun, Philipp Krähenbühl,
arXiv techical report (arXiv 2105.00636)
This repo contains code for our paper Learning to drive from a world on rails.
ProcGen code coming soon.
If you find our repo or paper useful, please cite us as
@inproceedings{chen2021learning,
title={Learning to drive from a world on rails},
author={Chen, Dian and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
booktitle={ICCV},
year={2021}
}
If you evaluating the pretrained weights, make sure you are launching CARLA with -vulkan
!
python evaluate.py --agent-config=[PATH TO CONFIG]
python evaluate_nocrash.py --town={Town01,Town02} --weather={train, test} --agent-config=[PATH TO CONFIG] --resume
--agent=autoagents/lbc_agent
for LBC.python -m scripts.view_nocrash_results [PATH TO CONFIG.YAML]
We also release the data we trained for the leaderboard. Checkout DATASET.md for more details.
The leaderboard
codes are built from the original leaderboard repo.
The scenariorunner
codes are from the original scenario_runner repo.
The waypointer.py
GPS coordinate conversion codes are build from Marin Toromanoff's leadeboard submission.
This repo is released under the MIT License (please refer to the LICENSE file for details). The leaderboard repo which our leaderboard
folder builds upon is under the MIT License.