Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers: A Lagrange Multipliers Approach

In this paper, a reinforcement learning approach for designing feedback neural network controllers for nonlinear systems is proposed. Given a Signal Temporal Logic (STL) specification which needs to be satisfied by the system over a set of initial conditions, the neural network parameters are tuned...

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Veröffentlicht in:	ACM transactions on embedded computing systems 2019-10, Vol.18 (5s), p.1-20
Hauptverfasser:	Yaghoubi, Shakiba, Fainekos, Georgios
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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