GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915156373536768 |
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| author | Kazemkhani, Saman Pandya, Aarav Cornelisse, Daphne Shacklett, Brennan Vinitsky, Eugene |
| author_facet | Kazemkhani, Saman Pandya, Aarav Cornelisse, Daphne Shacklett, Brennan Vinitsky, Eugene |
| contents | 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. GPUDrive is a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine capable of generating 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. Despite these low-level optimizations, GPUDrive is fully accessible through Python, offering a seamless and efficient workflow for multi-agent, closed-loop simulation. Using GPUDrive, we train reinforcement learning agents on the Waymo Open Motion Dataset, achieving efficient goal-reaching in minutes and scaling to thousands of scenarios in hours. We open-source the code and pre-trained agents at https://github.com/Emerge-Lab/gpudrive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01584 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS Kazemkhani, Saman Pandya, Aarav Cornelisse, Daphne Shacklett, Brennan Vinitsky, Eugene Artificial Intelligence Hardware Architecture Graphics Performance 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. GPUDrive is a GPU-accelerated, multi-agent simulator built on top of the Madrona Game Engine capable of generating 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. Despite these low-level optimizations, GPUDrive is fully accessible through Python, offering a seamless and efficient workflow for multi-agent, closed-loop simulation. Using GPUDrive, we train reinforcement learning agents on the Waymo Open Motion Dataset, achieving efficient goal-reaching in minutes and scaling to thousands of scenarios in hours. We open-source the code and pre-trained agents at https://github.com/Emerge-Lab/gpudrive. |
| title | GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS |
| topic | Artificial Intelligence Hardware Architecture Graphics Performance |
| url | https://arxiv.org/abs/2408.01584 |