Method for controlling a turbine having a number of sensors for providing sensor values (z t) ascertained on the turbine and a number of actuators for actuating the turbine and that is characterized at each instant (t) from a plurality of chronological instants (t) of the control by a hidden state (s t) that can be derived by sensor values (z t) and a rating signal (r t) for the hidden state (s t) and can be influenced by alterable actuator values (a t) for the actuators, in which a) the dynamic behavior of the turbine is modeled with a recurrent neural network comprising an input layer, a recurrent hidden layer and an output layer on the basis of training data comprising sensor values (z t), actuator values (a t) and rating signals (r t), wherein: i) the input layer is formed from first vectors of neurons that describe sensor values (z t) and/or actuator values (a t) at the instants (t), ii) the recurrent hidden layer is formed from second vectors of neurons that describe the hidden state (s t) of the turbine at the instants (t), wherein chronologically, for all the instants (t), two respective vectors from the second vectors are connected to a first connection (B, E, G, H) that spans one instant (t) and, additionally chronologically, two respective vectors from the second vectors are connected to a second connection (C, K) that spans at least two instants (t), iii) the output layer is formed from at least one third vector of neurons that describe the rating signal (r t) or at least one portion of the sensor values (z t) and/or at least one portion of the actuator values (a t) at the instants (t), b) a learning and/or optimization method is performed on the hidden states (s t) in order to provide a set of rules having optimized actuator values (a t) for each of the hidden states (s t), c) the current hidden state (s t) is ascertained by means of the recurrent neural network and currently ascertained sensor values (z t) from the sensors, and d) current actuator values (a t) are ascertained in order to actuate the actuators by means of the provided set of rules and the current hidden state (s t). a) the dynamic behavior of the turbine is modeled with a recurrent neural network comprising an input layer, a recurrent hidden layer and an output layer on the basis of training data comprising sensor values (z t), actuator values (a t) and rating signals (r t), wherein: i) the input layer is formed from first vectors of neurons that describe sensor values (z t) and/or actuator values (a t) at the instants (t), ii) the recurrent hidden layer is formed from second vectors of neurons that describe the hidden state (s t) of the turbine at the instants (t), wherein chronologically, for all the instants (t), two respective vectors from the second vectors are connected to a first connection (B, E, G, H) that spans one instant (t) and, additionally chronologically, two respective vectors from the second vectors are connected to a second connection (C, K) that spans at least two instants (t), iii) the output layer is formed from at least one third vector of neurons that describe the rating signal (r t) or at least one portion of the sensor values (z t) and/or at least one portion of the actuator values (a t) at the instants (t), i) the input layer is formed from first vectors of neurons that describe sensor values (z t) and/or actuator values (a t) at the instants (t), ii) the recurrent hidden layer is formed from second vectors of neurons that describe the hidden state (s t) of the turbine at the instants (t), wherein chronologically, for all the instants (t), two respective vectors from the second vectors are connected to a first connection (B, E, G, H) that spans one instant (t) and, additionally chronologically, two respective vectors from the second vectors are connected to a second connection (C, K) that spans at least two instants (t), iii) the output layer is formed from at least one third vector of neurons that describe the rating signal (r t) or at least one portion of the sensor values (z t) and/or at least one portion of the actuator values (a t) at the instants (t), b) a learning and/or optimization method is performed on the hidden states (s t) in order to provide a set of rules having optimized actuator values (a t) for each of the hidden states (s t), c) the current hidden state (s t) is ascertained by means of the recurrent neural network and currently ascertained sensor values (z t) from the sensors, and d) current actuator values (a t) are ascertained in order to actuate the actuators by means of the provided set of rules and the current hidden state (s t).