The reliability of railway traffic is commonly evaluated with train punctuality, where the\ud
deviations of actual train arrivals/departures and train arrivals/departures published in the\ud
timetable are compared. Minor train delays can be mitigated or even eliminated with running\ud
time supplements, while major delays can lead to so-called secondary delays of other trains\ud
on the network. Railway lines with high capacity utilization are more likely subject to delays,\ud
since a greater number of trains means a larger number of potential conflicts and more\ud
interactions between trains. Consequently, the secondary delays are harder to limit. Railway\ud
manager and carrier personnel are responsible for safe, undisturbed and punctual railway\ud
traffic. But unforeseen events can lead to delays, which calls for train rescheduling, where\ud
new train arrivals and departures are calculated. Train rescheduling is a complex\ud
optimization problem, currently solved based on dispatcher’s expert knowledge. With the\ud
increasing number of trains the complexity of the problem grows, the need for a decision\ud
support system increases. Train rescheduling is considered an NP-complete problem, where\ud
conventional mathematical and computer optimization methods fail to find the optimal\ud
solution, but artificial intelligence approaches have some measure of success. In this\ud
dissertation an algorithm for train rescheduling based on reinforcement learning, more\ud
precisely Q-learning, was developed. The Q-learning agent learns from rewards and\ud
punishments received from the environment, and looks for the optimal train dispatching\ud
strategy depending on the objective function
