Synthetic aperture imaging system is the trend of the next generation optical telescope. The imaging quality of the system can be guaranteed only when the piston errors between sub apertures are corrected within a certain range. A precise common phase piston error correction method based on Q-Learning with a correction range of ±λ/2 is proposed. The image clarity of the synthetic aperture imaging system is taken as the reward function, and the optical path compensation control action of the sub aperture is combined with the state to improve the decision-making ability of the agent in continuous learning, so as to generate the optimal control strategy, and quickly correct the piston. Through the simulation environment, the established agent model can obtain the state-value table in Q-learning in the environment with piston error, and can obtain the fast correction path sequence from the initial state to the best imaging performance through the q-table. At the same time, it is not limited by the target scene, which verifies the effectiveness and reliability of this method.