MetaDrive Documentation

Welcome to the MetaDrive documentation! MetaDrive is an efficient and compositional 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 1,500 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.

This documentation brings you the information on installation, usages and more of MetaDrive!

You can also visit the GitHub repo of MetaDrive. Please feel free to contact us if you have any suggestions or ideas!

We also make a video to benchmark the FPS of MetaDrive in different platforms. It can run at +2000FPS at MacBook Pro. Please checkout the YouTube video.

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]

And more:

  • Quanyi Li, Zhenghao Peng, Haibin Wu, Lan Feng, Bolei Zhou. “Human-AI Shared Control via Policy Dissection.” (NeurIPS 2022)

  • Yang, Yujie, Yuxuan Jiang, Yichen Liu, Jianyu Chen, and Shengbo Eben Li. “Model-Free Safe Reinforcement Learning through Neural Barrier Certificate.” IEEE Robotics and Automation Letters (2023).

  • Feng, Lan, Quanyi Li, Zhenghao Peng, Shuhan Tan, and Bolei Zhou. “TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios.” (ICRA 2023)

  • Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, Bolei Zhou. “Guarded Policy Optimization with Imperfect Online Demonstrations.” (ICLR 2023)


You can read our white paper describing the details of MetaDrive! If you use MetaDrive in your own work, please cite:

  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},