1. A computer-implemented method for a computer-aided control and/or regulation of a technical system, comprising: representing in a plurality of data sets based on observed data for the technical system a dynamic behavior of the technical system for a plurality of different points in time by a state of the technical system and an action executed on the technical system, with a respective action at a respective time leading to a follow-up state of the technical system at a next point in time; implementing reinforcement learning via a neural network executed on a processor of a computer to derive an optimum action selection rule, the reinforcement learning implemented on the plurality of data sets, each data set including the state at a respective point in time, the action executed in the state at the point in time, and the follow-up state and whereby each data set is assigned an evaluation, the reinforcement learning of the optimum action selection rule based on rewards that depend on a quality function for the state and action and on a value function for the follow-up state, comprising: (a) modeling of the quality function by the neural network reflecting a quality of an action for the plurality of states and the plurality of actions of the technical system, and (b) determining parameters of the neural network by reinforced learning of the neural network on the basis of an optimality criterion that depends on the plurality of evaluations of the plurality of data sets and the quality function; and regulating and/or controlling the technical system by selecting the plurality actions to be carried out on the technical system using the learned optimum action selection rule based on the learned neural network.