Cooperation in learning improves the speed of convergence and the quality of learning. Special treatment is needed when heterogeneous agents cooperate in learning. It has been discussed that, cooperation in learning may cause the learning process not to converge if heterogeneity is not handled properly. In this paper, it is assumed that two (or several) heterogeneous Q-learning agents cooperate to learn. The two hunter agents independently pursue a prey agent on a two-dimensional lattice: however, the hunters' visual-field depths are different. Thus, in order to have successful cooperation, the agents should be able to interpret other agents' Q-table. For this purpose, an algorithm has been proposed and implemented on the pursuit problem. Two case studies has been introduced and simulated to show the effectiveness of the proposed algorithm.