  In this work, we propose a self-improving artificial intelligence system to
enhance the safety performance of reinforcement learning (RL)-based autonomous
driving (AD) agents using black-box verification methods. RL algorithms have
become popular in AD applications in recent years. However, the performance of
existing RL algorithms heavily depends on the diversity of training scenarios.
A lack of safety-critical scenarios during the training phase could result in
poor generalization performance in real-world driving applications. We propose
a novel framework in which the weaknesses of the training set are explored
through black-box verification methods. After discovering AD failure scenarios,
the RL agent's training is re-initiated via transfer learning to improve the
performance of previously unsafe scenarios. Simulation results demonstrate that
our approach efficiently discovers safety failures of action decisions in
RL-based adaptive cruise control (ACC) applications and significantly reduces
the number of vehicle collisions through iterative applications of our method.
The source code is publicly available at
https://github.com/data-and-decision-lab/self-improving-RL.
