Learning and optimization of stochastic systems is a multi-disciplinary area that attracts researchers in control systems, operations research, and computer science. Areas such as perturbation analysis (PA), Markov decision processes (MDP), and reinforcement learning (RL) share a common goal. This chapter offers an overview of the area of learning and optimization from a system theoretic perspective, and it is shown that these seemingly different fields are actually closely related. Furthermore, this perspective leads to new research directions, which are illustrated using a queueing example. The central piece of this area is the performance potentials, which can be equivalently represented as perturbation realization factors that measure the effects of a single change to a sample path on the system performance. Potentials or realization factors can be used as building blocks to construct performance sensitivities. These sensitivity formulas serve as the basis for learning and optimization.