  We propose a framework for synthesizing a feedback control policy that
maximizes the region of attraction (ROA) of a closed-loop nonlinear dynamical
system. Our synthesis technique relies on stochastic optimization, which
involves computation of an objective function capturing the ROA for a feedback
control law. We employ a machine learning technique based on deep neural
network to estimate the ROA for a given feedback controller. Overall, our
technique is capable of synthesizing a controller co-optimizing traditional
control objectives like LQR cost together with ROA. We demonstrate the efficacy
of our technique through exhaustive experiments carried out on various
nonlinear systems.
