Computer-implemented method for controlling a regulating device of a conveyor system for the transport of piece goods of at least one type, in particular mail items and pieces of luggage, the conveyor system having a plurality of conveyor elements aligned along and parallel to a conveying direction, the conveyor elements being driven, under the control of the control device, by a respectively assigned drive at an individually adjustable speed v in order to achieve an alignment of the conveyor elements with respect to the conveying direction drive assigned in each case at an individually adjustable speed v in order to achieve an alignment and/or a defined distance and/or a defined spacing of the piece goods, characterized in that the control of the regulating device is determined by at least one is determined by at least one operator acting or learning methods, which agent, in accordance with a strategy, situationally selects an action from an action space for an initial state in order to reach a subsequent state, the states being defined with state vectors -s (t), s (t+ Δt) - and the actions with action vectors -a (t), a (t+ Δt) - can be mapped, comprising the method steps: a) creating an output image of the conveyor system; b) for each of the unit loads (4 n) on the image, individually creating a state vector s n (t) of predefined dimension and of the same dimension for all unit loads (4, 4 n) of one type, comprising the immediately comprising state information of the respective piece goods (4 n) taken from the immediately previously created image; c) for each piece (4 n) individually selecting an action vector a n (t) from an action space in accordance with the strategy, which is the same for all pieces of a kind, for the current state vector s n (t) of this piece good (4 n), the dimension of the action vector a n (t) being predetermined; d) for each piece goods (4 n) mapping the action vector a n (t) onto the real conveying elements of this piece goods (4 n), in order to determine the velocity v of these conveying elements and to and corresponding control of the conveying elements with the control device; e) after a cycle time Δt has elapsed, producing a sequential image of the conveying system image of the conveyor system and carrying out method step b) in order to obtain a state vector of the subsequent state s n (t+ Δt) for each piece goods (4 n); f) if the strategy for piece goods of one kind is to be trained further during the execution of the method the state vector of the subsequent state s n (t+ Δt) is determined for each piece (4 n) of this type by a method of reinforcement learning. (t+ Δt) is evaluated for each item (4 n) of this kind by a method of reinforcement learning, whereupon the agent trains and thus optimizes its strategy for items of this kind, by adjusting the action vectors to (t) of the action space; g) for each unit load (4 n), re-executing the procedure steps c) - f) using the improved or predetermined strategy as long as the unit load (4 n) in question is mapped on the subsequent image. a) creating an output image of the conveyor system; b) for each of the unit loads (4 n) on the image, individually creating a state vector s n (t) of predefined dimension and of the same dimension for all unit loads (4, 4 n) of one type, comprising the immediately comprising state information of the respective piece goods (4 n) taken from the immediately previously created image; c) for each piece (4 n) individually selecting an action vector a n (t) from an action space in accordance with the strategy, which is the same for all pieces of a kind, for the current state vector s n (t) of this piece good (4 n), the dimension of the action vector a n (t) being predetermined; d) for each piece goods (4 n) mapping the action vector a n (t) onto the real conveying elements of this piece goods (4 n), in order to determine the velocity v of these conveying elements and to and corresponding control of the conveying elements with the control device; e) after a cycle time Δt has elapsed, producing a sequential image of the conveying system image of the conveyor system and carrying out method step b) in order to obtain a state vector of the subsequent state s n (t+ Δt) for each piece goods (4 n); f) if the strategy for piece goods of one kind is to be trained further during the execution of the method the state vector of the subsequent state s n (t+ Δt) is determined for each piece (4 n) of this type by a method of reinforcement learning. (t+ Δt) is evaluated for each item (4 n) of this kind by a method of reinforcement learning, whereupon the agent trains and thus optimizes its strategy for items of this kind, by adjusting the action vectors to (t) of the action space; g) for each unit load (4 n), re-executing the procedure steps c) - f) using the improved or predetermined strategy as long as the unit load (4 n) in question is mapped on the subsequent image.