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Challenges and Opportunities of Applying Reinforcement Learning to Autonomous Racing.
Peter
R. Wurman, Peter Stone, and Michael Sprannger.
IEEE Intelligent
Systems, 37(3):20–3, May-June 2022.
Official
online version
(unavailable)
Simulated motorsports are an exciting environment in which to explore the power and limitations of deep reinforcement learning. Racing requires precise control of a vehicle that is operating at its traction limits while competing wheel-to-wheel with other drivers. We recently demonstrated an agent that can beat the best drivers in the world at the racing game Gran Turismo. In this article, we briefly discuss some of the lessons learned and some of the remaining open research challenges.
@article{IEEE-IS22,
author={Peter R.\ Wurman and Peter Stone and Michael Sprannger},
journal="{IEEE} {I}ntelligent {S}ystems",
title="Challenges and Opportunities of Applying Reinforcement Learning to Autonomous Racing",
year={2022},
month={May-June},
volume={37},
number={3},
pages={20--3},
abstract="Simulated motorsports are an exciting environment in which
to explore the power and limitations of deep reinforcement
learning. Racing requires precise control of a vehicle
that is operating at its traction limits while competing
wheel-to-wheel with other drivers. We recently
demonstrated an agent that can beat the best drivers in
the world at the racing game Gran Turismo. In this
article, we briefly discuss some of the lessons learned
and some of the remaining open research challenges.",
wwwnote={<a href="https://ieeexplore.ieee.org/abstract/document/9839480">Official online version</a>},
}
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