MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

Quanyi Li1,4*,  Zhenghao Peng1*,  Lan Feng3Qihang Zhang1Zhenghai Xue1Bolei Zhou2 
1The Chinese University of Hong Kong, 2University of California, Los Angeles 3ETH Zurich
4Centre for Perceptual and Interactive Intelligence
Webpage | Code | Paper
MetaDrive Simulator

To facilitate the research of generalizable reinforcement learning, we develop an open-source, highly efficient and flexible driving simulator MetaDrive, which holds the following key features:

We construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic.

Please refer to the technical report or the code repo for more information.
      title={MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning},
      author={Quanyi Li and Zhenghao Peng and Zhenghai Xue and Qihang Zhang and Bolei Zhou},