Method for computer-assisted determining of the occupancy state of a track section (GA) with the steps of • the physical wheel influence of trains (ZUG) travelling on the track section (GA) is converted into a directional voltage (RSP), • the directional voltage (RSP) is read into a processor (PRC) as raw data (RDT) via an analogue-digital converter (ADU), • an assessment to check whether or not a wheel influence is present takes place with the aid of at least one algorithm, and an influence state is derived therefrom, • the influence state is output to determine the occupancy state, characterised in that to identify wheel influences as part of the assessment of the algorithm, a reinforcement learning is used to optimise the parameters taken into consideration for the identification, wherein • the algorithm uses neural networks for the reinforcement learning (VSL), • the method steps are performed in parallel in a first subsystem (TS1) as well as in a second subsystem (TS2) and the first subsystem (TS1) as well as the second subsystem (TS2) both output the influence state to an evaluation facility (AWE) and the evaluation facility (AWE) compares an actual occupancy state with the influence state, • the evaluation facility (AWE) ascertains the actual occupancy state by evaluation of the data of sensor facilities, in particular axle counters, on the track section (GA) or of external data, in particular timetable data (FPD) • the evaluation facility (AWE) reports the actual occupancy state or an item of information with regard to the actual occupancy state corresponding to the output influence state back to the neural networks of the first subsystem (TS1) as well as of the second subsystem (TS2), • in the event that one of the subsystems of the first subsystem (TS1) as well as of the second subsystem (TS2), by way of the reinforcement learning (VSL), has achieved a higher level of certainty in relation to misinterpretations during the ascertaining of the influence state than the other, then the parameters taken into consideration for the identifying are transferred to the other subsystem (TS1, TS2). • the physical wheel influence of trains (ZUG) travelling on the track section (GA) is converted into a directional voltage (RSP), • the directional voltage (RSP) is read into a processor (PRC) as raw data (RDT) via an analogue-digital converter (ADU), • an assessment to check whether or not a wheel influence is present takes place with the aid of at least one algorithm, and an influence state is derived therefrom, • the influence state is output to determine the occupancy state, • the algorithm uses neural networks for the reinforcement learning (VSL), • the method steps are performed in parallel in a first subsystem (TS1) as well as in a second subsystem (TS2) and the first subsystem (TS1) as well as the second subsystem (TS2) both output the influence state to an evaluation facility (AWE) and the evaluation facility (AWE) compares an actual occupancy state with the influence state, • the evaluation facility (AWE) ascertains the actual occupancy state by evaluation of the data of sensor facilities, in particular axle counters, on the track section (GA) or of external data, in particular timetable data (FPD) • the evaluation facility (AWE) reports the actual occupancy state or an item of information with regard to the actual occupancy state corresponding to the output influence state back to the neural networks of the first subsystem (TS1) as well as of the second subsystem (TS2), • in the event that one of the subsystems of the first subsystem (TS1) as well as of the second subsystem (TS2), by way of the reinforcement learning (VSL), has achieved a higher level of certainty in relation to misinterpretations during the ascertaining of the influence state than the other, then the parameters taken into consideration for the identifying are transferred to the other subsystem (TS1, TS2). <EOS>