  Every day, railways experience disturbances and disruptions, both on the
network and the fleet side, that affect the stability of rail traffic. Induced
delays propagate through the network, which leads to a mismatch in demand and
offer for goods and passengers, and, in turn, to a loss in service quality. In
these cases, it is the duty of human traffic controllers, the so-called
dispatchers, to do their best to minimize the impact on traffic. However,
dispatchers inevitably have a limited depth of perception of the knock-on
effect of their decisions, particularly how they affect areas of the network
that are outside their direct control. In recent years, much work in Decision
Science has been devoted to developing methods to solve the problem
automatically and support the dispatchers in this challenging task. This paper
investigates Machine Learning-based methods for tackling this problem,
proposing two different Deep Q-Learning methods(Decentralized and Centralized).
Numerical results show the superiority of these techniques with respect to the
classical linear Q-Learning based on matrices. Moreover, the Centralized
approach is compared with a MILP formulation showing interesting results. The
experiments are inspired by data provided by a U.S. Class 1 railroad.
