Resetting in stochastic optimal control

``When in a difficult situation, it is sometimes better to give up and start all over again.'' While this empirical truth has been regularly observed wide range of circumstances, quantifying the effectiveness such heuristic strategy remains an open challenge. In paper, we combine notions optimal control stochastic resetting address problem. The emerging analytical framework allows one not only measure performance given restarting policy, but also obtain for class dynamical systems. We apply our technique system with final reward show that value must be larger than critical threshold effective. Our approach, analogous celebrated Hamilton-Jacobi-Bellman paradigm, provides basis investigation realistic strategies across disciplines. As application, can applied epidemic model predict lockdown policy.