Abstract: Training data sets which are obtained by controlling the machine by different control systems are read in, the training data sets each including a state data set and an action data set. Furthermore, a performance evaluator is provided and determines, for a control agent, a performance for controlling the machine by the control agent. A control-system-specific control agent for the different control systems is respectively trained to reproduce an action data set on the basis of a state data set. In addition, a respective environment is delimited on the basis of a distance dimension in a parameter space of the control-system-specific control agents. Test control agents, for each of which a performance value is determined by the performance evaluator, are then generated within the environments. Depending on the determined performance values, a performance-optimizing control agent is finally selected from the test control agents and is used to control the machine.

Abstract: To configure a control agent, predefined training data are read in, which specify state datasets, action datasets and resulting performance values of the technical system. Using the training data, a data-based dynamic model is trained to reproduce a resulting performance value using a state dataset and an action dataset. An action evaluation process is also trained to reproduce the action dataset using a state dataset and an action dataset after an information reduction has been carried out, wherein a reproduction error is determined. To train the control agent, training data are supplied, the trained action evaluation process and the control agent. Performance values output by the trained dynamic model are fed into a predefined performance function. Reproduction errors are fed as performance-reducing influencing variables into the performance function. The control agent is trained to output an action dataset optimising the performance function on the basis of a state dataset.