Method for computer-aided control and/or regulation of a technical system, wherein: a) the technical system is characterised, for a plurality of time points (t), in each case, by a state (x t) with a number of state variables and an action (a t) carried out on the technical system with a number of action variables and an evaluation signal (r t) for the state (x t) and the action (a t); b) the dynamic behaviour of the technical system is modelled with a recurrent neural network comprising an input layer, a recurrent hidden layer and an output layer based on training data comprising known states (x t), actions (a t) and evaluation signals (r t), wherein: i) the input layer is formed by a first state space with a first dimension which comprises the states (x t) of the technical system and the actions (a t) performed on the technical system; ii) the recurrent hidden layer is formed by a second state space with a second dimension and comprises hidden states (s t, s t i, s t *, s t **) with a number of hidden state variables; iii) the output layer is formed by a third state space with a third dimension which is defined such that the states thereof represent the evaluation signals (r t) or exclusively those state and/or action variables which influence the evaluation signals (r t); c) a learning and/or optimisation process is performed on the hidden states (s t) in the second state space for controlling and/or regulating the technical system by carrying out actions (a t) on the technical system. a) the technical system is characterised, for a plurality of time points (t), in each case, by a state (x t) with a number of state variables and an action (a t) carried out on the technical system with a number of action variables and an evaluation signal (r t) for the state (x t) and the action (a t); b) the dynamic behaviour of the technical system is modelled with a recurrent neural network comprising an input layer, a recurrent hidden layer and an output layer based on training data comprising known states (x t), actions (a t) and evaluation signals (r t), wherein: i) the input layer is formed by a first state space with a first dimension which comprises the states (x t) of the technical system and the actions (a t) performed on the technical system; ii) the recurrent hidden layer is formed by a second state space with a second dimension and comprises hidden states (s t, s t i, s t *, s t **) with a number of hidden state variables; iii) the output layer is formed by a third state space with a third dimension which is defined such that the states thereof represent the evaluation signals (r t) or exclusively those state and/or action variables which influence the evaluation signals (r t); i) the input layer is formed by a first state space with a first dimension which comprises the states (x t) of the technical system and the actions (a t) performed on the technical system; ii) the recurrent hidden layer is formed by a second state space with a second dimension and comprises hidden states (s t, s t i, s t *, s t **) with a number of hidden state variables; iii) the output layer is formed by a third state space with a third dimension which is defined such that the states thereof represent the evaluation signals (r t) or exclusively those state and/or action variables which influence the evaluation signals (r t); c) a learning and/or optimisation process is performed on the hidden states (s t) in the second state space for controlling and/or regulating the technical system by carrying out actions (a t) on the technical system.