https://github.com/decisionforce/pgdrive
PGDrive: an open-ended driving simulator with infinite scenes from procedural generation
https://github.com/decisionforce/pgdrive
autonomous-driving computer-vision deep-learning generalization imitation-learning machine-learning multi-agent panda3d procedural-generation reinforcement-learning simulation simulator
Last synced: 18 days ago
JSON representation
PGDrive: an open-ended driving simulator with infinite scenes from procedural generation
- Host: GitHub
- URL: https://github.com/decisionforce/pgdrive
- Owner: decisionforce
- License: apache-2.0
- Created: 2020-11-21T08:12:29.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2022-06-20T12:14:48.000Z (over 3 years ago)
- Last Synced: 2025-08-25T11:53:48.438Z (5 months ago)
- Topics: autonomous-driving, computer-vision, deep-learning, generalization, imitation-learning, machine-learning, multi-agent, panda3d, procedural-generation, reinforcement-learning, simulation, simulator
- Language: Python
- Homepage: https://decisionforce.github.io/pgdrive/
- Size: 75.4 MB
- Stars: 130
- Watchers: 7
- Forks: 16
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# PGDrive: an open-ended driving simulator with infinite scenes
[](http://github.com/decisionforce/pgdrive/actions)
[](https://codecov.io/gh/decisionforce/pgdrive)
[](https://pgdrive.readthedocs.io)
[](https://github.com/decisionforce/pgdrive/blob/main/LICENSE.txt)
[](https://github.com/decisionforce/pgdrive/stargazers)
**This project is deprecated and merged into [MetaDrive](https://github.com/decisionforce/metadrive). Please follow the MetaDrive repo for the latest development and maintenance.**
**[ 📺 [Website](https://decisionforce.github.io/pgdrive/) | 🏗 [Github Repo](https://github.com/decisionforce/pgdrive) | 📜 [Documentation](https://pgdrive.readthedocs.io/) | 🎓 [Paper](https://arxiv.org/pdf/2012.13681) ]**
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.
## 🛠 Quick Start
Please install PGDrive via:
```bash
pip install pgdrive
```
If you wish to contribute to this project or make some modification, you can clone the latest version of PGDrive locally and install via:
```bash
git clone https://github.com/decisionforce/pgdrive.git
cd pgdrive
pip install -e .
```
You can verify the installation and efficiency of PGDrive via running:
```bash
python -m pgdrive.examples.profile_pgdrive
```
The 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`.
## 🚕 Examples
Please run the following line to drive the car in the environment manually with keyboard!
```bash
python -m pgdrive.examples.enjoy_manual
```
You can also enjoy a journey carrying out by our professional driver pretrained from reinforcement learning!
```bash
python -m pgdrive.examples.enjoy_expert
```
A fusion of expert and manual controller,
where the expect will try to rescue the manually controlled vehicle from danger,
can be experienced via:
```bash
python -m pgdrive.examples.enjoy_saver
```
To show the main feature, procedural generation, we provide a script to show BIG:
```bash
python -m pgdrive.examples.render_big
```
*Note 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:
```bash
python -m pgdrive.examples.draw_maps
```
To build the environment in python script, you can simply run:
```python
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:
[](https://colab.research.google.com/github/decisionforce/PGDrive/blob/main/pgdrive/examples/Basic%20PGDrive%20Usages.ipynb)
## 📦 Predefined environment sets
We also define several Gym environment names, so user can start training in the minimalist manner:
```python
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* |
## 🏫 Documentations
More information about PGDrive can be found in [PGDrive Documentation](https://pgdrive.readthedocs.io).
Besides, the training code of our [paper](https://arxiv.org/pdf/2012.13681) can be found in [this repo](https://github.com/decisionforce/pgdrive-generalization-paper).
## 📎 Citation
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}
}
```
[](https://www.codacy.com/gh/decisionforce/pgdrive/dashboard?utm_source=github.com&utm_medium=referral&utm_content=decisionforce/pgdrive&utm_campaign=Badge_Grade)
[](https://github.com/decisionforce/pgdrive/graphs/contributors)
[](https://github.com/decisionforce/pgdrive/network)
[](https://github.com/decisionforce/pgdrive/issues)