Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions

Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning priority scores and choosing the highest-ranked item. In this work, we propose a factorized scheduling principle (FSP) framework to learn interpretabl