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A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo.
Miguel Vasco, Takuma
Seno, Kenta Kawamoto, Kaushik Subramanian, Peter R. Wurman, and Peter
Stone.
In Reinforcement Learning Conference (RLC), August 2024.
arXiv
version
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.
@InProceedings{RLC24-sophy,
author="Miguel Vasco and Takuma Seno and Kenta Kawamoto and Kaushik Subramanian and Peter R.\ Wurman and Peter Stone",
title="A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in {G}ran {T}urismo",
booktitle="Reinforcement Learning Conference (RLC)",
month="August",
year="2024",
location="Amherst, MA, USA",
abstract={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.},
wwwnote={<a href="http://arxiv.org/abs/2406.12563">arXiv version</a>},
}
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