  We develop a method for obtaining safe initial policies for reinforcement
learning via approximate dynamic programming (ADP) techniques for uncertain
systems evolving with discrete-time dynamics. We employ kernelized Lipschitz
estimation and semidefinite programming for computing admissible initial
control policies with provably high probability. Such admissible controllers
enable safe initialization and constraint enforcement while providing
exponential stability of the equilibrium of the closed-loop system.
