GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
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.
66b193fbf9c20a095dc1d3f7
Saman Kazemkhani
66b193fbf9c20a095dc1d3f8
Aarav Pandya
66b193fbf9c20a095dc1d3f9
Daphne Cornelisse
66b193fbf9c20a095dc1d3fa
Brennan Shacklett
admin_assigned
2024-08-06T11:57:56.210Z
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/avatars/fd62fab336564aaf92bdc1272ec13bcc.svg
Brennan Shacklett
user
shacklettbp
66b193fbf9c20a095dc1d3fb
Eugene Vinitsky
admin_assigned
2024-08-06T11:58:01.885Z
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/avatars/4ab66412c837eeca5fd4775684ce6026.svg
Eugene Vinitsky
user
eugenevinitsky
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
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.
GPUDrive, a GPU-accelerated multi-agent simulator, enables efficient training of reinforcement learning agents on the Waymo Motion dataset, reducing training time significantly.
GPUDrive multi-agent simulator Madrona Game Engine C++ functions CUDA reinforcement learning agents Waymo Motion dataset goal-reaching agents highly effective agents