Nowadays, learning of agents gets more and more useful in game environments. But it takes a long learning time to produce satisfactory results in game. So, we need a good method to shorten the learning time. In this paper, we present a strategy for improving the learning performance of Q-learning with prediction information. It refers to the chosen action at each status in the Q-learning algorithm, It stores the referred value at the P-table of prediction module, and then it searches some values with high frequency at the table. The values are used to renew second compensation value from the Q-table. Our experiments show that our approach gets the efficiency improvement of average 9% after the middle point of learning experiments, and that the more actions in a status space, the higher performance.