Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem
This work addresses train scheduling efficiency for railway operators, but it appears incremental as it builds on existing reinforcement learning frameworks with a new feature extraction method.
The paper tackled train rescheduling by proposing a reinforcement learning algorithm that uses standardized feature extraction from pairwise conflicts, and the results showed that this feature space enabled learning a sensible scheduling policy as indicated by empirical tests using Flatland Challenge metrics.
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.