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Presents a method for control of autonomous mobile robots to acquire fine actions based on real-time search. In the proposed method, the information criterion for the environment is defined based on the Kullback-Leibler divergence, which measures the quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information which is improved step by step in the sense of this criterion. According to this, we can harmonize the trade-off between the calculation amount and information quality, and make the search process faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is shown through some simulation results.