[Paper] Stratifying Reinforcement Learning with Signal Temporal Logic

Summary

This paper proposes a novel stratification-based semantics for Signal Temporal Logic (STL), interpreting atomic predicates as membership tests in a stratified space. This new perspective reveals a correspondence between STL and stratification theory, suggesting that most STL formulas induce a stratification of space-time. This offers a fresh theoretical framework for analyzing STL's structure within reinforcement learning contexts.

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