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This paper focuses on the learning action selection in behavior-based autonomous mobile robot. Autonomous mobile robot needs a large space to store the state-action pair in the application of tabular Q-learning. Neural network has a good ability of generalization, so in this paper Q-learning based on neural network is developed which has a good ability to approximate to Q-function. The Q-learning based on neural network is applied to autonomous mobile robot for goal directed obstacle avoidance. Results of simulation show that the mobile robot can learn to select proper actions itself to accomplish the task autonomously.