MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

[ Documentation | Demo Video | Website | Paper ]

MetaDrive is a driving simulator with the following key features:

Compositional: It supports generating infinite scenes with various road maps and traffic settings for the research of generalizable RL.
Lightweight: It is easy to install and run. It can run up to 300 FPS on a standard PC.
Realistic: Accurate physics simulation and multiple sensory input including Lidar, RGB images, top-down semantic map and first-person view images.

🛠 Quick Start

Install MetaDrive via:

git clone https://github.com/decisionforce/metadrive.git
cd metadrive
pip install -e .

pip install metadrive-simulator

Note that the program is tested on both Linux and Windows. Some control and display issues in MacOS wait to be solved

You can verify the installation of MetaDrive via running the testing script:

# Go to a folder where no sub-folder calls metadrive
python -m metadrive.examples.profile_metadrive

Note that please do not run the above command in a folder that has a sub-folder called ./metadrive.

🚕 Examples

Run the following command to launch a simple driving scenario with auto-drive mode on. Press W, A, S, D to drive the vehicle manually.

python -m metadrive.examples.drive_in_single_agent_env

Run the following command to launch a safe driving scenario, which includes more complex obstacles and cost to be yielded.

python -m metadrive.examples.drive_in_safe_metadrive_env

You can also launch an instance of Multi-Agent scenario as follows

python -m metadrive.examples.drive_in_multi_agent_env --env roundabout

or launch and render in pygame front end

python -m metadrive.examples.drive_in_multi_agent_env --pygame_render --env roundabout

env argument could be:

roundabout (default)
intersection
tollgate
bottleneck
parkinglot
pgmap

Run the example of procedural generation of a new map as:

python -m metadrive.examples.procedural_generation

Note that the above four scripts can not be ran in a headless machine. Please refer to the installation guideline in documentation for more information about how to launch runing in a headless machine.

Run the following command to draw the generated maps from procedural generation:

python -m metadrive.examples.draw_maps

To build the RL environment in python script, you can simply code in the OpenAI gym format as:

import metadrive  # Import this package to register the environment!
import gym

env = gym.make("MetaDrive-v0", config=dict(use_render=True))
# env = metadrive.MetaDriveEnv(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()

🏫 Documentations

Find more details in: MetaDrive

📎 References

If you use MetaDrive in your own work, please cite:

@article{li2021metadrive,
  title={MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning},
  author={Li, Quanyi and Peng, Zhenghao and Xue, Zhenghai and Zhang, Qihang and Zhou, Bolei},
  journal={arXiv preprint arXiv:2109.12674},
  year={2021}
}

🎉 Relevant Projects

Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization
Zhenghao Peng, Quanyi Li, Chunxiao Liu, Bolei Zhou
NeurIPS 2021
[Paper] [Code] [Webpage] [Poster] [Talk]

Safe Driving via Expert Guided Policy Optimization
Zhenghao Peng*, Quanyi Li*, Chunxiao Liu, Bolei Zhou
Conference on Robot Learning (CoRL) 2021
[Paper] [Code] [Webpage] [Poster]

Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Quanyi Li*, Zhenghao Peng*, Bolei Zhou
ICLR 2022
[Paper] [Code] [Webpage]

1abner1/metadrive