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Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/3358239","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T13:13:05Z","timestamp":1570713185000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers"],"prefix":"10.1145","volume":"18","author":[{"given":"Shakiba","family":"Yaghoubi","sequence":"first","affiliation":[{"name":"CIDSE, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Fainekos","sequence":"additional","affiliation":[{"name":"CIDSE, Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,10,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Houssam Abbas Matthew O\u2019Kelly Alena Rodionova and Rahul Mangharam. 2017. 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