Interactive assistance system for computer-aided control optimization for a technical system, having a) an input terminal (IN) for interactively reading in at least one state parameter, which indicates a first system state (S 0 ) of the technical system, and at least one setting parameter (W) for setting up a reward function (RF) suitable for training a neural network, b) a simulation module (SIM) for simulating performance of an action sequence (A) on the technical system starting from the first system state (S 0 ) and for predicting resulting successor states (S) of the technical system, wherein the state parameter is transmitted to the simulation module (SIM), and a neural network which is pretrained for the technical system and is intended to simulate the technical system is implemented in the simulation module (SIM), c) an optimization module (OPT) which is coupled to the input terminal (IN) and to the simulation module (SIM) and is intended i) to set up the reward function (RF) on the basis of the setting parameter (W), ii) to generate a multiplicity of action sequences (A) for the first system state (S 0 ), iii) to transmit the action sequences (A) to the simulation module (SIM) and to receive the resulting successor states (S), iv) to determine rewards which can be expected for the resulting successor states (S) by means of the reward function (RF) which has been set up, and v) to determine a reward-optimizing action sequence (A'), wherein the optimization is logically separate from the neural simulation, and d) an output terminal (OUT) for outputting a system state (S') resulting from the reward-optimizing action sequence (A'). a) an input terminal (IN) for interactively reading in at least one state parameter, which indicates a first system state (S 0 ) of the technical system, and at least one setting parameter (W) for setting up a reward function (RF) suitable for training a neural network, b) a simulation module (SIM) for simulating performance of an action sequence (A) on the technical system starting from the first system state (S 0 ) and for predicting resulting successor states (S) of the technical system, wherein the state parameter is transmitted to the simulation module (SIM), and a neural network which is pretrained for the technical system and is intended to simulate the technical system is implemented in the simulation module (SIM), c) an optimization module (OPT) which is coupled to the input terminal (IN) and to the simulation module (SIM) and is intended i) to set up the reward function (RF) on the basis of the setting parameter (W), ii) to generate a multiplicity of action sequences (A) for the first system state (S 0 ), iii) to transmit the action sequences (A) to the simulation module (SIM) and to receive the resulting successor states (S), iv) to determine rewards which can be expected for the resulting successor states (S) by means of the reward function (RF) which has been set up, and v) to determine a reward-optimizing action sequence (A'), wherein the optimization is logically separate from the neural simulation, and i) to set up the reward function (RF) on the basis of the setting parameter (W), ii) to generate a multiplicity of action sequences (A) for the first system state (S 0 ), iii) to transmit the action sequences (A) to the simulation module (SIM) and to receive the resulting successor states (S), iv) to determine rewards which can be expected for the resulting successor states (S) by means of the reward function (RF) which has been set up, and v) to determine a reward-optimizing action sequence (A'), wherein the optimization is logically separate from the neural simulation, and d) an output terminal (OUT) for outputting a system state (S') resulting from the reward-optimizing action sequence (A'). <EOS>