Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Autonomous Overtaking in Gran Turismo Sport Using Curriculum
  Reinforcement Learning   Professional race-car drivers can execute extreme overtaking maneuvers.
However, existing algorithms for autonomous overtaking either rely on
simplified assumptions about the vehicle dynamics or try to solve expensive
trajectory-optimization problems online. When the vehicle approaches its
physical limits, existing model-based controllers struggle to handle highly
nonlinear dynamics, and cannot leverage the large volume of data generated by
simulation or real-world driving. To circumvent these limitations, we propose a
new learning-based method to tackle the autonomous overtaking problem. We
evaluate our approach in the popular car racing game Gran Turismo Sport, which
is known for its detailed modeling of various cars and tracks. By leveraging
curriculum learning, our approach leads to faster convergence as well as
increased performance compared to vanilla reinforcement learning. As a result,
the trained controller outperforms the built-in model-based game AI and
achieves comparable overtaking performance with an experienced human driver.

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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys