  Deep reinforcement learning (DRL) has achieved groundbreaking successes in a
wide variety of robotic applications. A natural consequence is the adoption of
this paradigm for safety-critical tasks, where human safety and expensive
hardware can be involved. In this context, it is crucial to optimize the
performance of DRL-based agents while providing guarantees about their
behavior. This paper presents a novel technique for incorporating domain-expert
knowledge into a constrained DRL training loop. Our technique exploits the
scenario-based programming paradigm, which is designed to allow specifying such
knowledge in a simple and intuitive way. We validated our method on the popular
robotic mapless navigation problem, in simulation, and on the actual platform.
Our experiments demonstrate that using our approach to leverage expert
knowledge dramatically improves the safety and the performance of the agent.
