title: GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
summary: 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
  https://github.com/Emerge-Lab/gpudrive.
paper_authors._id: 66b193fbf9c20a095dc1d3f7
paper_authors.name: Saman Kazemkhani
paper_authors._id: 66b193fbf9c20a095dc1d3f8
paper_authors.name: Aarav Pandya
paper_authors._id: 66b193fbf9c20a095dc1d3f9
paper_authors.name: Daphne Cornelisse
paper_authors._id: 66b193fbf9c20a095dc1d3fa
paper_authors.name: Brennan Shacklett
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T11:57:56.210Z
paper_authors._id: 644c431f9a7c19cfaf0c3800
paper_authors.avatarUrl: /avatars/fd62fab336564aaf92bdc1272ec13bcc.svg
paper_authors.fullname: Brennan Shacklett
paper_authors.type: user
paper_authors.user: shacklettbp
paper_authors._id: 66b193fbf9c20a095dc1d3fb
paper_authors.name: Eugene Vinitsky
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T11:58:01.885Z
paper_authors._id: 6456cf4c4a7ffb7d5a4af7b7
paper_authors.avatarUrl: /avatars/4ab66412c837eeca5fd4775684ce6026.svg
paper_authors.fullname: Eugene Vinitsky
paper_authors.type: user
paper_authors.user: eugenevinitsky
paper_title: GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
paper_summary: 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
  https://github.com/Emerge-Lab/gpudrive.
paper_ai_keywords: GPU-accelerated Madrona Game Engine C++ CUDA reinforcement learning Waymo Motion Quality goal-reaching agents