A semi-hierarchical RL approach for railway rescheduling nearly doubles the number of trains reaching destinations in simulations while keeping deadlock rates below 5%.
Efficient multi-objective optimisation for real-world power grid topology control
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
A semi-hierarchical RL approach for railway rescheduling nearly doubles the number of trains reaching destinations in simulations while keeping deadlock rates below 5%.