TY - GEN
T1 - Worst-case Satisfaction of STL Specifications Using Feedforward Neural Network Controllers
T2 - 2020 Information Theory and Applications Workshop, ITA 2020
AU - Yaghoubi, Shakiba
AU - Fainekos, Georgios
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2/2
Y1 - 2020/2/2
N2 - 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.
AB - 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.
KW - Reinforcement Learning
KW - Signal Temporal Logic
KW - neural network controller
KW - • Computer systems organization ? Robotic control
KW - • Theory of computation ? Adversarial learning
UR - http://www.scopus.com/inward/record.url?scp=85097350477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097350477&partnerID=8YFLogxK
U2 - 10.1109/ITA50056.2020.9244969
DO - 10.1109/ITA50056.2020.9244969
M3 - Conference contribution
AN - SCOPUS:85097350477
T3 - 2020 Information Theory and Applications Workshop, ITA 2020
BT - 2020 Information Theory and Applications Workshop, ITA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 February 2020 through 7 February 2020
ER -