Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain
  Randomization Reinforcement Learning   How to explore corner cases as efficiently and thoroughly as possible has
long been one of the top concerns in the context of deep reinforcement learning
(DeepRL) autonomous driving. Training with simulated data is less costly and
dangerous than utilizing real-world data, but the inconsistency of parameter
distribution and the incorrect system modeling in simulators always lead to an
inevitable Sim2real gap, which probably accounts for the underperformance in
novel, anomalous and risky cases that simulators can hardly generate. Domain
Randomization(DR) is a methodology that can bridge this gap with little or no
real-world data. Consequently, in this research, an adversarial model is put
forward to robustify DeepRL-based autonomous vehicles trained in simulation to
gradually surfacing harder events, so that the models could readily transfer to
the real world.

12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org