  Recent advances in Reinforcement Learning (RL) combined with Deep Learning
(DL) have demonstrated impressive performance in complex tasks, including
autonomous driving. The use of RL agents in autonomous driving leads to a
smooth human-like driving experience, but the limited interpretability of Deep
Reinforcement Learning (DRL) creates a verification and certification
bottleneck. Instead of relying on RL agents to learn complex tasks, we propose
HPRL - Hierarchical Program-triggered Reinforcement Learning, which uses a
hierarchy consisting of a structured program along with multiple RL agents,
each trained to perform a relatively simple task. The focus of verification
shifts to the master program under simple guarantees from the RL agents,
leading to a significantly more interpretable and verifiable implementation as
compared to a complex RL agent. The evaluation of the framework is demonstrated
on different driving tasks, and NHTSA precrash scenarios using CARLA, an
open-source dynamic urban simulation environment.
