metadrive-simulator 0.2.5.3

An open-ended driving simulator with infinite scenes

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL

[ Documentation | Colab Examples | Demo Video | Website | Paper | Relevant Projects ]

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/metadriverse/metadrive.git
cd metadrive
pip install -e .

or

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

We provide examples to demonstrate features and basic usages of MetaDrive after the local installation. Or you can run some examples directly in Colab.

Single Agent Environment

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

Multi-Agent Environment

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

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

--env accepts following parmeters: roundabout (default), intersection, tollgate, bottleneck, parkinglot, pgmap. Adding --pygame_render can launch top-down pygame renderer.

Real Environment

Running the following script enables driving in a scenario constructed from Waymo motion dataset.

python -m metadrive.examples.drive_in_waymo_env

Press key r for loading a new scenario, and b or q for switching perspective.

Basic Usage

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{li2022metadrive,
  title={Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning},
  author={Li, Quanyi and Peng, Zhenghao and Feng, Lan and Zhang, Qihang and Xue, Zhenghai and Zhou, Bolei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022}
}

🎉 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] [Poster] [Talk]

Human-AI Shared Control via Policy Dissection
Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou
NeurIPS 2022
[Paper] [Code] [Webpage]