MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
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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.
@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} }