Method for operating a technical system, comprising computer-supported learning of an optimum-certainty action selection rule that maximizes the performance of the technical system by taking into account the statistical uncertainty, wherein the operation of the technical system is characterized by states (s) that the technical system can adopt during operation and actions that are performed during the operation of the technical system and transfer a respective state (s) of the technical system to a subsequent state, in which: - variables containing training data that record states (s), actions (a) and subsequent states (s') are each captured with statistical uncertainty during operation of the technical system, wherein a covariance matrix that covers the correlations between uncertainties of the training data's variables included in the learning process is produced for the statistical uncertainties of the captured variables; - a quality function is learned on the basis of the training data captured during operation of the technical system, wherein the quality function models optimum operation of the technical system; - a measure (σQ) of the uncertainty of the quality function is ascertained during the learning of the quality function by means of an uncertainty propagation by means of the covariance matrix, which propagates uncertainties in the captured training data into the quality function, wherein the uncertainty propagation uses the covariance matrix, in which the non-diagonal elements are ignored, as a result of which correlations between the variables taken into account for the uncertainty propagation are ignored, and the measure (σQ) of the statistical uncertainty and a certainty parameter (ξ), which corresponds to a statistical minimum demand on the quality function, are taken as a basis for determining a modified quality function, - wherein an action selection rule (π(s)) is learned on the basis of the modified quality function, which indicates the action (a) to be performed or actions (a) to be performed for a respective state (s) of the technical system during operation of the technical system, wherein the technical system is operated on the basis of the learned control and/or regulation by virtue of the action (a) to be performed being selected using the learned action selection rule in a respective state (s) of the technical system. - variables containing training data that record states (s), actions (a) and subsequent states (s') are each captured with statistical uncertainty during operation of the technical system, wherein a covariance matrix that covers the correlations between uncertainties of the training data's variables included in the learning process is produced for the statistical uncertainties of the captured variables; - a quality function is learned on the basis of the training data captured during operation of the technical system, wherein the quality function models optimum operation of the technical system; - a measure (σQ) of the uncertainty of the quality function is ascertained during the learning of the quality function by means of an uncertainty propagation by means of the covariance matrix, which propagates uncertainties in the captured training data into the quality function, wherein the uncertainty propagation uses the covariance matrix, in which the non-diagonal elements are ignored, as a result of which correlations between the variables taken into account for the uncertainty propagation are ignored, and the measure (σQ) of the statistical uncertainty and a certainty parameter (ξ), which corresponds to a statistical minimum demand on the quality function, are taken as a basis for determining a modified quality function, - wherein an action selection rule (π(s)) is learned on the basis of the modified quality function, which indicates the action (a) to be performed or actions (a) to be performed for a respective state (s) of the technical system during operation of the technical system,