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

• URL: http://arxiv.org/abs/2408.01584v2
• Date: Thu, 3 Oct 2024 22:18:54 GMT
• Title: GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
• Authors: Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky,
• Abstract summary: GPUDrive is a GPU-accelerated, multi-agent simulator built on top of the Madrona Engine Game. We show that using GPUDrive we can effectively train reinforcement learning agents over many scenes in the Open Motion dataset.
• Score: 4.172988187048097
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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