In this paper, we propose a new method to specify the sequence of parameter values for a fuzzy clustering algorithm by using Q-learning. In the clustering algorithm, we employ similarities between two data points and distances from data to cluster centers as the fuzzy clustering criteria. The fuzzy clustering is achieved by optimizing an objective function, which is solved by the Picard iteration. The fuzzy clustering algorithm might be useful but its result depends on the parameter specifications. To conquer the dependency on the parameter values, we use Q-learning to learn the sequential update for the parameters during the iterative optimization procedure of the fuzzy clustering. In the numerical example, we show how the clustering validity improves by the obtained parameter update sequences.