On Robustness Metrics for Learning STL Tasks

Autor:	Varnai, Peter, Dimarogonas, Dimos V.
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	Signal temporal logic (STL) is a powerful tool for describing complex behaviors for dynamical systems. Among many approaches, the control problem for systems under STL task constraints is well suited for learning-based solutions, because STL is equipped with robustness metrics that quantify the satisfaction of task specifications and thus serve as useful rewards. In this work, we examine existing and potential robustness metrics specifically from the perspective of how they can aid such learning algorithms. We show that various desirable properties restrict the form of potential metrics, and introduce a new one based on the results. The effectiveness of this new robustness metric for accelerating the learning procedure is demonstrated through an insightful case study. Comment: The paper has been accepted for publishing in the Proceedings of the 2020 American Control Conference (ACC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2003.06041 Zobrazit plný text záznamu View this record from Arxiv