In order to solve the problem of dimension disaster which may be produced by applying Q-learing to intelligent
system of continuous state-space, this paper proposes a  Q-learning algorithm based on ART 2 and gives the specific steps. Through introducing the ART 2 neural network in the Q-learning algorithm, Q-learning Agent in view of the duty learns an appropriate incremental clustering of state-space model, so Agent can carry out decision-making and a two-tiers online learning of state-space model cluster in unknown environment without any priori knowledge. Through the interaction with the environment unceasingly alternately to improve the control strategies, the learning accuracy is increased. Finally, the mobile robot navigation simulation experiments show that, using the ARTQL algorithm, motion robot can improve its navigation performance continuously by interactive learning with the environment.