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

Shakiba Yaghoubi, Georgios Fainekos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 in order to maximize the satisfaction of the STL formula. The framework is based on a max-min formulation of the robustness of the STL formula. The maximization is solved through a Lagrange multipliers method, while the minimization corresponds to a falsification problem. We present our results on a vehicle and a quadrotor model and demonstrate that our approach reduces the training time more than 50 percent compared to the baseline approach.

Original languageEnglish (US)
Title of host publication2020 Information Theory and Applications Workshop, ITA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141909
DOIs
StatePublished - Feb 2 2020
Event2020 Information Theory and Applications Workshop, ITA 2020 - San Diego, United States
Duration: Feb 2 2020Feb 7 2020

Publication series

Name2020 Information Theory and Applications Workshop, ITA 2020

Conference

Conference2020 Information Theory and Applications Workshop, ITA 2020
CountryUnited States
CitySan Diego
Period2/2/202/7/20

Keywords

  • Reinforcement Learning
  • Signal Temporal Logic
  • neural network controller
  • • Computer systems organization ? Robotic control
  • • Theory of computation ? Adversarial learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization

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