This project is deprecated and merged into MetaDrive. Please follow the MetaDrive repo for the latest development and maintenance.
[ 📺 Website | 🏗 Github Repo | 📜 Documentation | 🎓 Paper ]
Welcome to PGDrive! PGDrive is an driving simulator with many key features, including:
- 🎏 Lightweight: Extremely easy to download, install and run in almost all platforms.
- 📷 Realistic: Accurate physics simulation and multiple sensory inputs.
- 🚀 Efficient: Up to 500 simulation step per second and easy to parallel.
- 🗺 Open-ended: Support generating infinite scenes and configuring various traffic, vehicle, and environmental settings.
Please install PGDrive via:
pip install pgdriveIf you wish to contribute to this project or make some modification, you can clone the latest version of PGDrive locally and install via:
git clone https://github.com/decisionforce/pgdrive.git
cd pgdrive
pip install -e .You can verify the installation and efficiency of PGDrive via running:
python -m pgdrive.examples.profile_pgdriveThe above script is supposed to be runnable in all places.
Note that please do not run the above command in the folder that has a sub-folder called ./pgdrive.
Please run the following line to drive the car in the environment manually with keyboard!
python -m pgdrive.examples.enjoy_manualYou can also enjoy a journey carrying out by our professional driver pretrained from reinforcement learning!
python -m pgdrive.examples.enjoy_expertA fusion of expert and manual controller, where the expect will try to rescue the manually controlled vehicle from danger, can be experienced via:
python -m pgdrive.examples.enjoy_saverTo show the main feature, procedural generation, we provide a script to show BIG:
python -m pgdrive.examples.render_bigNote that the above three scripts can not be run in headless machine. Please refer to the installation guideline in documentation for more information.
Running the following line allows you to draw the generated maps:
python -m pgdrive.examples.draw_mapsTo build the environment in python script, you can simply run:
import pgdrive # Import this package to register the environment!
import gym
env = gym.make("PGDrive-v0", config=dict(use_render=True))
# env = pgdrive.PGDriveEnv(config=dict(environment_num=100)) # Or build environment from class
env.reset()
for i in range(1000):
obs, reward, done, info = env.step(env.action_space.sample()) # Use random policy
env.render()
if done:
env.reset()
env.close()We also prepare a Colab which demonstrates some basic usage of PGDrive as follows:
We also define several Gym environment names, so user can start training in the minimalist manner:
import gym
import pgdrive # Register the environment
env = gym.make("PGDrive-v0")The following table presents some predefined environment names.
| Gym Environment Name | Random Seed Range | Number of Maps | Comments |
|---|---|---|---|
PGDrive-test-v0 |
[0, 200) | 200 | Test set, not change for all experiments. |
PGDrive-validation-v0 |
[200, 1000) | 800 | Validation set. |
PGDrive-v0 |
[1000, 1100) | 100 | Default training setting, for quick start. |
PGDrive-10envs-v0 |
[1000, 1100) | 10 | Training environment with 10 maps. |
PGDrive-1000envs-v0 |
[1000, 1100) | 1000 | Training environment with 1000 maps. |
PGDrive-training0-v0 |
[3000, 4000) | 1000 | First set of 1000 environments. |
PGDrive-training1-v0 |
[5000, 6000) | 1000 | Second set of 1000 environments. |
PGDrive-training2-v0 |
[7000, 8000) | 1000 | Thirds set of 1000 environments. |
| ... | More map set can be added in response to the requests |
More information about PGDrive can be found in PGDrive Documentation. Besides, the training code of our paper can be found in this repo.
If you find this work useful in your project, please consider to cite it through:
@article{li2020improving,
title={Improving the Generalization of End-to-End Driving through Procedural Generation},
author={Li, Quanyi and Peng, Zhenghao and Zhang, Qihang and Qiu, Cong and Liu, Chunxiao and Zhou, Bolei},
journal={arXiv preprint arXiv:2012.13681},
year={2020}
}
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