This paper deals with the analysis of nonlinear reinforcement schemes for learning automata. The learning automaton is connected in feedback loop to a random environment. The correction term of the action probability vector depends on a nonlinear function phi(x). Results concerning the convergence, the convergence rate, and the effect of the function phi(x) are stated. A comparison between the convergence rate of nonlinear and linear reinforcement schemes is presented.