  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.
