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Computer Science > Artificial Intelligence

arXiv:2408.01584 (cs)
[Submitted on 2 Aug 2024]

Title:GPUDrive: Data-driven, multi-agent driving simulation at 1
million FPS

Authors:Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan
Shacklett, Eugene Vinitsky
View a PDF of the paper titled GPUDrive: Data-driven, multi-agent
driving simulation at 1 million FPS, by Saman Kazemkhani and 4 other
authors
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    Abstract:Multi-agent learning algorithms have been successful at
    generating superhuman planning in a wide variety of games but
    have had little 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 this
    scale, we present GPUDrive, a GPU-accelerated, multi-agent
    simulator built on top of the Madrona Game Engine that can
    generate over a million steps of experience 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 are able to effectively train reinforcement
    learning agents over many scenes in the Waymo Motion dataset,
    yielding highly effective goal-reaching agents in minutes for
    individual scenes and generally capable agents in a few hours. We
    ship these trained agents as part of the code base at this https
    URL.

Comments: 8 pages, 4 figures
Subjects: Artificial Intelligence (cs.AI); Hardware Architecture
          (cs.AR); Graphics (cs.GR); Performance (cs.PF)
Cite as:  arXiv:2408.01584 [cs.AI]
          (or arXiv:2408.01584v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2408.01584
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Daphne Cornelisse [view email]
[v1] Fri, 2 Aug 2024 21:37:46 UTC (2,710 KB)
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