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We propose a train rescheduling algorithm which applies a standardizedfeature selection based on pairwise conflicts in order to serve as input forthe reinforcement learning framework. We implement an analytical method whichidentifies and optimally solves every conflict arising between two trains, thenwe design a corresponding observation space which features the most relevantinformation considering these conflicts. The data obtained this way thentranslates to actions in the context of the reinforcement learning framework.We test our preliminary model using the evaluation metrics of the FlatlandChallenge. The empirical results indicate that the suggested feature spaceprovides meaningful observations, from which a sensible scheduling policy canbe learned.

Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem