Method for the computer-assisted control and/or regulation of a technical system where the dynamic behavior of the technical system is modeled using a recurrent neural network

A computer program product with program code stored on a non-transitory machine-readable medium, when the program executes on a processor of a computer, the program comprising:

• modeling a dynamic behavior of a technical system, wherein the technical system is described by a state (xt) of the technical system and an action (at) performed on the technical system for a plurality of points in time (t), with a respective action (at) leading at a respective point in time (t) into a new state, (xt+1) of the technical system at a next point in time (t+1);
• wherein the modeling of the dynamic behavior of the technical system uses a recurrent neural network using training data, the recurrent neural network includes a plurality of states (xt) and a plurality of actions (at) determined using a simulation model at a plurality of different times (t), wherein the recurrent neural network is formed by a first input layer (I) including the first plurality of states (xt) and the first plurality of actions (at) performed on the technical system for the plurality of different times (t), a hidden recurrent layer (H) including a plurality of hidden states (st, pt), and an output layer (O) including the first plurality of states (xt) for the plurality of different times (t);
• learning an action selection rule by the recurrent neural network by coupling the recurrent neural network to a further neural network for a current and future times, wherein the further neural network comprises a feed-forward network that includes a second input layer, a second hidden layer (R) including a second plurality of hidden states (rt) and a second output layer (O′),
• wherein the further neural network uses as its second input layer a respective part of the first plurality of hidden states (pt) of the hidden recurrent layer (H) at a respective point in time (t), which is coupled to the second hidden layer (R), which is coupled to the second output layer (O′) that comprises a predicted action (at) to be performed on the technical system at respective point in time, the predicted action (at) fed forward to the hidden state (st) of the hidden recurrent layer (H) such that the predicted actions learned by the further neural network rather than externally input future actions are used for the learning the actin selection rule;
• and
• determining the state of the technical system and the action to be performed on the technical system by the recurrent neural network and coupled to the further neural network using a plurality of learned action selection rules.