Abstract: An operating signal is fed to a first machine learning module to reproduce a behavior signal of a technical system, the behavior signal occurring specifically without the current use of a control action and output the reproduced behavior signal as a first output signal. The first output signal is fed to a second machine learning module to reproduce a resulting behavior signal using a control action signal and output the reproduced behavior signal as a second output signal. Furthermore, an operating signal is fed to a third machine learning module, and a third output signal is fed to the trained second machine learning module. A control action performance ascertained using the second output signal, and the control action performance is used to train the third machine learning module to optimize the control action performance. By training the third machine learning module, a control device controls the technical system.

Abstract: A controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window-specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.

Abstract: A method is provided for the computer-aided open-loop and/or closed-loop control of the operation of an energy generation system.

Abstract: To control a technical system, training data are read in, a training dataset, including in each case a state dataset, an action dataset and a resulting performance value of the technical system. Using the training data, a first machine learning module is trained to reproduce a resulting performance value on the basis of a state dataset and an action dataset. State datasets are also supplied to different deterministic control agents and resulting output data are fed into the trained first machine learning module as action data sets. Depending on performance values output by the trained first machine learning module, several control agents are then selected. The technical system is controlled in each case by the selected control agents, wherein further state datasets, action datasets and performance values are captured and added to the training data. Using the training data, the method steps are repeated.

Abstract: A method is provided for the computer-aided open-loop and/or closed-loop control of the operation of an energy generation system.

Abstract: An operating signal is fed to a first machine learning module to reproduce a behavior signal of a technical system, the behavior signal occurring specifically without the current use of a control action and output the reproduced behavior signal as a first output signal. The first output signal is fed to a second machine learning module to reproduce a resulting behavior signal using a control action signal and output the reproduced behavior signal as a second output signal. Furthermore, an operating signal is fed to a third machine learning module, and a third output signal is fed to the trained second machine learning module. A control action performance ascertained using the second output signal, and the control action performance is used to train the third machine learning module to optimize the control action performance. By training the third machine learning module, a control device controls the technical system.

Abstract: A computer-implemented method for configuring a controller for a technical system is provided. The controller controls the technical system based on an output data set determined by the controller for an input data set, wherein the method includes: training a first data driven model with training data including several pre-known input data sets and corresponding pre-known output data sets for the respective pre-known input data sets, where the first data driven model predicts respective future values of one or more target variables for one or more subsequent time points; training a second data driven model with the training data using reinforcement learning with a reward depending on the respective future values of the one or more target variables which are predicted by the trained first data driven model, where the trained second data driven model determines the output data set for the input data set within the controller.

Abstract: A controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.

Abstract: A control device for a technical system, state-specific safety information about an admissibility of a control action signal is read in by a safety module is provided. Furthermore, a state signal indicating a state of the technical system is supplied to a machine learning module and to the safety module. In addition, an output signal of the machine learning module is supplied to the safety module. The output signal is converted into an admissible control action signal by the safety module on the basis of the safety information depending on the state signal. Furthermore, a performance for control of the technical system by the admissible control action signal is ascertained, and the machine learning module is trained to optimize the performance. The control device is then configured by the trained machine learning module.