1. A method for a computer-aided learning of a control of a technical system, comprising: characterizing an operation of the technical system by a state; executing an action during the operation for converting the state into a subsequent state; generating a training data comprising the state, the action and the subsequent state; modeling an optimal operation of the technical system by a quality function; learning the quality function based on the training data; determining a measure for a statistical uncertainty of the quality function by an uncertainty propagation; defining a modified quality function depending on the measure and a certainty parameter corresponding to a statistical minimum requirement for the quality function; specifying the action by an action selection rule; and learning the action selection rule based on the modified quality function.