In this research, an autonomous control system for blimp navigation was developed using reinforcement learning algorithm. The aim of this research is to provide a blimp the capability to approach a goal position autonomously in an environment, where the dynamical models of the blimp and the environment are unknown. Webots™ Robotics Simulator (WRS) was used to simulate and evaluate the control strategy obtained through a one-step Q-learning method. The simulation data generated via WRS were then processed and analysed within MATLAB. The simulation results showed that the control policy acquired from Q-learning is much more effective compared to the traditional control methods.