## 1 Introduction  
### Motivation  
Railway networks can ensure environmentally friendly transportation for both passengers and freight traffic. Due to the climatic targets, an increase in the volume transported by rail and average speed is desirable. Accordingly, the main objectives of the European Union are greening the freight transport by doubling rail freight traffic by 2050, boosting the innovation in the sector and bring the data-based solutions and artificial intelligence in the fore [1]. The possible data-driven solutions in the field of energy efficiency and their future are detailed in [2]. In addition, reinforcement learning is also an important part of artificial intelligence from an optimization point of view, which can also provide a solution to several challenges in the field of rail transportation, as summarized in [3]. One such major challenge is how to increase the capacity of the railway lines to achieve these goals. In urban areas, the number of tracks is limited and not expendable at reasonable costs. The first possibility to increase the volume is the density of scheduled trains. However, this method can cause more delays, waiting times, and decreased average speed in disturbing situations. Despite this, real-time automated rescheduling of trains may help increase the railway lines' capacity. Other capacity-raising methods are the expensive automatic train control (ATC) and automatic train operation (ATO) to minimize driver errors. This method can help operators to make the right decisions to schedule. This process means reordering and rescheduling tasks in a perturbed situation based on railway safety regulations-- route-based interlocking rules. Every deviation from the pre-planned conflict-free timetable may lead to delays, which can cause secondary delays for conflicted trains. In the case of a disturbance, the traffic situation--the perturbed situation--can be solved only with timetable modifications, which is a minor change in the pre-planned timetable. Rescheduling and reordering the trains will cause a new, locally conflict-free solution. The impacts of this change may lead to other conflicts, especially in a single-track line, which is the root cause of the secondary delays. The effectiveness of the solution depends on the size of the controlled area, in which the reordering and rescheduling task is performed. Disruptions are events in that solutions require additional methods, and the effects are longer, e.g., in case of a collision with a road vehicle, the reallocation of the rolling stock is needed. The operators cancel other trains, and the recovery of the pre-planned timetable means several hours or days. Nowadays, human operators make real-time scheduling based on the traffic ordinance and their knowledge and experiences. During the decision-making process, human errors may occur. Therefore, automated decision-making algorithms are needed to efficiently solve the rescheduling and reordering tasks. Besides the traditional optimization-based solutions (e.g., mixed integer linear programming or heuristic methods), AI technology may help railway operators. Automated rescheduling algorithms may run in the operational control centers (OCC), where the operators supervise a large remote-controlling area. The efficiency of these algorithms depends on their run-time and feasibility. The latter means the ability to suit the railway safety rules and the quality of the railway traffic, handling the connections, punctuality, realizing minimum delays based on the timetable and dwell times. The operators' decisions affect the exact route and speed of the trains. Consequently, the decision-making time may be crucial when the operator makes the decision. The execution of the decision depends on the acceleration and deceleration capabilities of the locos, the limits of the train controlling systems (ATP, ATC, ATO), and the exact time of the decision.