Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding Failures in High-Fidelity Simulation using Adaptive Stress
  Testing and the Backward Algorithm   Validating the safety of autonomous systems generally requires the use of
high-fidelity simulators that adequately capture the variability of real-world
scenarios. However, it is generally not feasible to exhaustively search the
space of simulation scenarios for failures. Adaptive stress testing (AST) is a
method that uses reinforcement learning to find the most likely failure of a
system. AST with a deep reinforcement learning solver has been shown to be
effective in finding failures across a range of different systems. This
approach generally involves running many simulations, which can be very
expensive when using a high-fidelity simulator. To improve efficiency, we
present a method that first finds failures in a low-fidelity simulator. It then
uses the backward algorithm, which trains a deep neural network policy using a
single expert demonstration, to adapt the low-fidelity failures to
high-fidelity. We have created a series of autonomous vehicle validation case
studies that represent some of the ways low-fidelity and high-fidelity
simulators can differ, such as time discretization. We demonstrate in a variety
of case studies that this new AST approach is able to find failures with
significantly fewer high-fidelity simulation steps than are needed when just
running AST directly in high-fidelity. As a proof of concept, we also
demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art
high-fidelity simulator for finding failures in autonomous vehicles.

12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio