This paper proposes a new intelligent control method for autonomous mobile robots to acquire appropriate actions based on a real-time action search. In the proposed method, information criterion on the virtual environment is defined based on Kullback-Leibler divergence, which measures quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information whose quality is improved step by step. By applying the proposed method, the trade-off between calculation amount and information quality is taken into account, and the search process converges faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is verified through some simulation results.