Growing environmental concerns drive the increasing need for a more climate-friendly mobility and pose a challenge for the development of future powertrains. Hydrogen engines represent a suitable alternative for the heavy-duty segment. However, typical operation includes dynamic conditions and the requirement for high loads that produce the highest NOx emissions. These emissions must be reduced below the legal limits through selective catalytic reduction (SCR). The application of such a control system is time-intensive and requires extensive domain knowledge. We propose that almost human-like control strategies can be achieved for this virtual application with less time and expert knowledge needed by using Deep Reinforcement Learning (DRL). A DRL agent is trained to control the injection of diesel exhaust fluid (DEF) and compared with the performance of a manually tuned controller. The performance is evaluated based on the restrictive emission limits of a possible EURO7-framework and DEF consumption. Applied to a standardized driving cycle (WHTC) and compared with the conventional application, the agent reaches similar emission values with an equally high DEF consumption. The results demonstrate, that the control of an exhaust gas aftertreatment system using DRL is very satisfactory. Furthermore, it is shown, that the methodology can be applied to different engine calibrations and still satisfactory results are achieved. Further work is required to refine the proposed methodology into a fully-fledged tool for application in powertrain development.