This article investigates the reinforcement-learning (RL)-based disturbance rejection control for uncertain nonlinear systems having nonsimple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the total uncertainty, which represents the perturbation to the nominal system dynamics. Based on the output of the observer, the control compensates for the total uncertainty in real time, and simultaneously, online approximates the optimal policy for the compensated system using a simulation of experience-based RL technique. Rigorous theoretical analysis is given to show the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy. It is worth mentioning that the widely used restrictive persistence of excitation (PE) condition is not required in the established framework. Simulation results are presented to illustrate the effectiveness of the proposed method.