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In this paper, a dynamic fuzzy Q-learning (DFQL) method navigating a mobile robot efficiently is presented. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions which capable of enabling us to deal with continuous-valued states and actions. Consequently, fuzzy rules can be generated automatically. Fuzzy inference systems provide a natural mean of incorporating the bias components for rapid reinforcement learning. Furthermore, the eligibility trace method is employed in our algorithm, leading to faster learning and alleviating the experimentation-sensitive problem where an arbitrarily bad training policy might result in a non-optimal policy. Experimental results demonstrate that the robot is able to learn the right policy with a few trials.