Abstract: The state and input constraints of nonlinear systems could greatly impede the realization of their optimal control when using reinforcement learning (RL)-based approaches since the commonly ...

Abstract: Many distributed optimization algorithms critically depend on careful step-size tuning to ensure stability and achieve fast convergence. In this work, we address this limitation in the ...