News

• Feb 10, 2022: a video shows MetaDrive efficiency can be found here! We can run ~2000FPS at Macbook Pro!
• Oct 22, 2022: Benchmark results of MARL environments are provided in metadrive-benchmark repo!
• May 3, 2022: MetaDrive 0.2.5 was released!
• Mar 28, 2022: MetaDrive white paper was accepted to TPAMI!

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:

• 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. The real traffic data replay is also supported.

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.

@article{li2021metadrive,
  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}
}