Abstract: Operating states and control actions specified in first training data sets are taken as a basis for assigning a respective operating state of the machine multiple control actions. This trains a machine learning module to take an operating state and a control action as a basis for predicting a performance of the machine that is accumulated over a state trajectory, which is constructed by determining a particular control action to be applied on the basis of output values of the machine learning module. A performance evaluator is used to determine a performance value. An accumulated performance for each of the alternative control actions associated with a respective current operating state of the machine is predicted. The machine is then actuated using a control action. If the optimizing control action is not executed successfully, another of the alternative control actions is selected.