AI RESEARCH
Stratifying Reinforcement Learning with Signal Temporal Logic
arXiv CS.LG
•
ArXi:2604.04923v1 Announce Type: new In this paper, we develop a stratification-based semantics for Signal Temporal Logic (STL) in which each atomic predicate is interpreted as a membership test in a stratified space. This perspective reveals a novel correspondence principle between stratification theory and STL, showing that most STL formulas can be viewed as inducing a stratification of space-time. The significance of this interpretation is twofold.