A novel multi-agent reinforcement learning approach is proposed to learn the coordinated behaviors among cooperative agents team.The proposed approach combines advantages of the modular architecture

profit-sharing learning and opponent modeling technique in a single multi-agent framework.Simulation results on the pursuit problem show that the proposed learning approach has faster convergence speed and more optimal policy over conventional modular Q-learning algorithms.