  Questions remain on the robustness of data-driven learning methods when
crossing the gap from simulation to reality. We utilize weight anchoring, a
method known from continual learning, to cultivate and fixate desired behavior
in Neural Networks. Weight anchoring may be used to find a solution to a
learning problem that is nearby the solution of another learning problem.
Thereby, learning can be carried out in optimal environments without neglecting
or unlearning desired behavior. We demonstrate this approach on the example of
learning mixed QoS-efficient discrete resource scheduling with infrequent
priority messages. Results show that this method provides performance
comparable to the state of the art of augmenting a simulation environment,
alongside significantly increased robustness and steerability.
