Patent ID: 8160978

Claim:
A method for computer-aided control of a technical system, comprising: characterizing a dynamic behavior of the technical system by a number of states and actions at a number of time points, a respective action at a respective time point resulting in a new state at a next time point; modeling the dynamic behavior with a recurrent neural network by a training data comprising known states and known actions at the number of time points, wherein the recurrent neural network comprises: an input layer comprising the states and the actions at the number of time points, a hidden recurrent layer comprising a number of hidden states at the number of time points, and an output layer comprising the states at the number of time points, wherein a respective hidden state at the respective time point comprises a first hidden state and a second hidden state at the respective time point, wherein a respective state in the input layer at the respective time point is associated with the first hidden state and the respective action in the input layer at the respective time point is associated with the second hidden state, and wherein the first hidden state is coupled to the second hidden by a matrix which is learned during the modeling; learning an action selection rule by coupling the recurrent neural network to a further neural network, wherein the further neural network comprises: a further input layer comprising the hidden states of the recurrent neural network, a further hidden layer comprising further hidden states, and a further output layer comprising the actions and changes of the actions compared with temporally preceding actions; and defining the states and the actions by coupling the recurrent neural network to the further neural network with the learned action selection rule.