A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914838946512896 |
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| author | Vasco, Miguel Seno, Takuma Kawamoto, Kenta Subramanian, Kaushik Wurman, Peter R. Stone, Peter |
| author_facet | Vasco, Miguel Seno, Takuma Kawamoto, Kenta Subramanian, Kaushik Wurman, Peter R. Stone, Peter |
| contents | Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this challenge in a high-fidelity racing simulator, Gran Turismo. However, this agent relied on global features that require instrumentation external to the car. This paper introduces, to the best of our knowledge, the first super-human car racing agent whose sensor input is purely local to the car, namely pixels from an ego-centric camera view and quantities that can be sensed from on-board the car, such as the car's velocity. By leveraging global features only at training time, the learned agent is able to outperform the best human drivers in time trial (one car on the track at a time) races using only local input features. The resulting agent is evaluated in Gran Turismo 7 on multiple tracks and cars. Detailed ablation experiments demonstrate the agent's strong reliance on visual inputs, making it the first vision-based super-human car racing agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12563 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo Vasco, Miguel Seno, Takuma Kawamoto, Kenta Subramanian, Kaushik Wurman, Peter R. Stone, Peter Machine Learning Computer Vision and Pattern Recognition Robotics Racing autonomous cars faster than the best human drivers has been a longstanding grand challenge for the fields of Artificial Intelligence and robotics. Recently, an end-to-end deep reinforcement learning agent met this challenge in a high-fidelity racing simulator, Gran Turismo. However, this agent relied on global features that require instrumentation external to the car. This paper introduces, to the best of our knowledge, the first super-human car racing agent whose sensor input is purely local to the car, namely pixels from an ego-centric camera view and quantities that can be sensed from on-board the car, such as the car's velocity. By leveraging global features only at training time, the learned agent is able to outperform the best human drivers in time trial (one car on the track at a time) races using only local input features. The resulting agent is evaluated in Gran Turismo 7 on multiple tracks and cars. Detailed ablation experiments demonstrate the agent's strong reliance on visual inputs, making it the first vision-based super-human car racing agent. |
| title | A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo |
| topic | Machine Learning Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2406.12563 |