Informed Reinforcement Learning for Situation-Aware Traffic Rule
  Exceptions
Daniel Bogdoll Jing Qin Moritz Nekolla Ahmed Abouelazm Tim Joseph J. Marius Zöllner
Reinforcement Learning is a highly active research field with promising
advancements. In the field of autonomous driving, however, often very simple
scenarios are being examined. Common approaches use non-interpretable control
commands as the action space and unstructured reward designs which lack
structure. In this work, we introduce Informed Reinforcement Learning, where a
structured rulebook is integrated as a knowledge source. We learn trajectories
and asses them with a situation-aware reward design, leading to a dynamic
reward which allows the agent to learn situations which require controlled
traffic rule exceptions. Our method is applicable to arbitrary RL models. We
successfully demonstrate high completion rates of complex scenarios with recent
model-based agents.