The problem of steering the state of nonlinearly perturbed linear systems by learning is investigated. A family of algorithms which compute the steering control by means of successive trials on the real plant is presented. Convergence in the face of a class of nonlinear plant perturbations is proven. State feedback linearizable systems are shown to be addressable by the presented algorithms. Two examples illustrate the applicability of algorithms.