  We propose a train rescheduling algorithm which applies a standardized
feature selection based on pairwise conflicts in order to serve as input for
the reinforcement learning framework. We implement an analytical method which
identifies and optimally solves every conflict arising between two trains, then
we design a corresponding observation space which features the most relevant
information considering these conflicts. The data obtained this way then
translates to actions in the context of the reinforcement learning framework.
We test our preliminary model using the evaluation metrics of the Flatland
Challenge. The empirical results indicate that the suggested feature space
provides meaningful observations, from which a sensible scheduling policy can
be learned.
