  Deep reinforcement learning has proven remarkably useful in training agents
from unstructured data. However, the opacity of the produced agents makes it
difficult to ensure that they adhere to various requirements posed by human
engineers. In this work-in-progress report, we propose a technique for
enhancing the reinforcement learning training process (specifically, its reward
calculation), in a way that allows human engineers to directly contribute their
expert knowledge, making the agent under training more likely to comply with
various relevant constraints. Moreover, our proposed approach allows
formulating these constraints using advanced model engineering techniques, such
as scenario-based modeling. This mix of black-box learning-based tools with
classical modeling approaches could produce systems that are effective and
efficient, but are also more transparent and maintainable. We evaluated our
technique using a case-study from the domain of internet congestion control,
obtaining promising results.
