GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
<|reference_start|>GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS: Multi-agent learning algorithms have been successful at generating superhuman planning in various games but have had limited impact on the design of deployed multi-agent planners. A key bottleneck in applying these techniques to multi-agent planning is that they require billions of steps of experience. To enable the study of multi-agent planning at scale, we present GPUDrive, a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine that can generate over a million simulation steps per second. Observation, reward, and dynamics functions are written directly in C++, allowing users to define complex, heterogeneous agent behaviors that are lowered to high-performance CUDA. We show that using GPUDrive we can effectively train reinforcement learning agents over many scenes in the Waymo Open Motion Dataset, yielding highly effective goal-reaching agents in minutes for individual scenes and enabling agents to navigate thousands of scenarios within hours. The code base with pre-trained agents is available at \url{https://github.com/Emerge-Lab/gpudrive}.<|reference_end|>
@article{kazemkhani2024gpudrive:,
    title={GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS},
    author={Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett,
  Eugene Vinitsky},
    journal={arXiv preprint arXiv:2408.01584},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01584},
    primaryClass={cs.AI cs.AR cs.GR cs.PF}
}