Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Safer Self-Driving Through Great PAIN (Physically Adversarial
  Intelligent Networks)   Automated vehicles' neural networks suffer from overfit, poor
generalizability, and untrained edge cases due to limited data availability.
Researchers synthesize randomized edge-case scenarios to assist in the training
process, though simulation introduces potential for overfit to latent rules and
features. Automating worst-case scenario generation could yield informative
data for improving self driving. To this end, we introduce a "Physically
Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact
aggressively in the CARLA simulation environment. We train two agents, a
protagonist and an adversary, using dueling double deep Q networks (DDDQNs)
with prioritized experience replay. The coupled networks alternately
seek-to-collide and to avoid collisions such that the "defensive" avoidance
algorithm increases the mean-time-to-failure and distance traveled under
non-hostile operating conditions. The trained protagonist becomes more
resilient to environmental uncertainty and less prone to corner case failures
resulting in collisions than the agent trained without an adversary.

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The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph