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 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.

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!

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]


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},