Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of the multiagent systems. However, most researches on multiagent system applying a reinforcement-learning algorithm focus on the method to reduce complexity due to the existence of multiple agents [4] and goals [8]. Though these predefined structures succeeded in putting down the undesirable effect due to the existence of multiple agents, they would also suppress the desirable emergence of cooperative behaviors in the multiagent domain. We show that the potential cooperative properties among the agent are emerged by means of Profit-sharing [2][3], which is robust in the non-MDPs.