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A Champion-Level Vision-Based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7.
Hojoon
Lee, Takuma Seno, Jun Jet Tai, Kaushik Subramanian, Kenta Kawamoto, Peter Stone,
and Peter R. Wurman.
IEEE Robotics and Automation Letters, 10(6):5545–52,
June 2025.
Deep reinforcement learning has achieved superhuman racing performance inhigh-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes globalfeatures that require instrumentation external to a car, such as preciselocalization of agents and opponents, limiting real-world applicability. Toaddress this limitation, we introduce a vision-based autonomous racing agent thatrelies solely on ego-centric camera views and onboard sensor data, eliminatingthe need for precise localization during inference. This agent employs anasymmetric actor-critic framework: the actor uses a recurrent neural network withthe sensor data local to the car to retain track layouts and opponent positions,while the critic accesses the global features during training. Evaluated in GT7,our agent consistently outperforms GT7's built-drivers. To our knowledge, thiswork presents the first vision-based autonomous racing agent to demonstratechampion-level performance in competitive racing scenarios.
@Article{peter_stone_ral_2025,
author = {Hojoon Lee and Takuma Seno and Jun Jet Tai and Kaushik Subramanian and Kenta Kawamoto and Peter Stone and Peter R.\ Wurman},
title = {A Champion-Level Vision-Based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7},
journal = {{IEEE} Robotics and Automation Letters},
month="June",
Volume="10",Number="6",
pages="5545--52",
doi="10.1109/LRA.2025.3560873",
year = {2025},
abstract = {Deep reinforcement learning has achieved superhuman racing performance in
high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global
features that require instrumentation external to a car, such as precise
localization of agents and opponents, limiting real-world applicability. To
address this limitation, we introduce a vision-based autonomous racing agent that
relies solely on ego-centric camera views and onboard sensor data, eliminating
the need for precise localization during inference. This agent employs an
asymmetric actor-critic framework: the actor uses a recurrent neural network with
the sensor data local to the car to retain track layouts and opponent positions,
while the critic accesses the global features during training. Evaluated in GT7,
our agent consistently outperforms GT7's built-drivers. To our knowledge, this
work presents the first vision-based autonomous racing agent to demonstrate
champion-level performance in competitive racing scenarios.
},
}
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