Summary of Gpudrive: Data-driven, Multi-agent Driving Simulation at 1 Million Fps, by Saman Kazemkhani et al.
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
by Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR); Graphics (cs.GR); Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel GPU-accelerated simulator, GPUDrive, is introduced to tackle the limitations of applying multi-agent learning algorithms to real-world planning problems. By leveraging the Madrona Game Engine and CUDA, GPUDrive enables the simulation of complex agent behaviors at unprecedented scales, generating over a million steps per second. This allows for efficient training of reinforcement learning agents on large datasets like Waymo Open Motion Dataset, achieving goal-reaching in minutes and scaling to thousands of scenarios in hours. The paper presents GPUDrive as an open-source tool, pre-trained agents available for download. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPUDrive is a special computer program that helps machines learn to work together by simulating many different situations really fast. This is important because it’s hard to teach machines to plan and make decisions when there are lots of them working together. The program uses super-powerful computers called GPUs to do the simulations, which makes it much faster than other programs. With GPUDrive, scientists can train machines to reach goals quickly and efficiently, even in very complex situations. |
Keywords
» Artificial intelligence » Reinforcement learning
