Abstract: The concept of reinforcement learning comes from behavior psychology that takes behavior learning as trial and error, by which the states of environment are mapped into corresponding actions. There’s a question of how the behaviorism can be used to learn the actions in interaction with the environment in designing intelligent robots. In this paper, the actions that a robot takes to avoid obstacles are taken as one class of behaviors and the reinforcement learning is used to realize behavior learning of obstacle avoidance. Q\|learning is one kind of reinforcement learning method that is similar to dynamic programming. After basic ideas of Q\|learning are introduced, a neural network learning algorithm of Q\|learning with concepts of competition and self\|organization is presented. Its application in local path planning of intelligent robots is also introduced. Finally, the detailed simulation results are presented.