Studies on multiagent systems have been widely studied and realized cooperative behaviors between agents, where many agents work together to achieve their objectives. In this paper, a new reinforcement learning framework considering the concept of "Symbiosis" in order to represent complicated relationships between agents and analyze the emerging behavior. In addition, distributed state-action value tables are also used to efficiently solve the multiagent problems with large number of

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... n pairs. From the simulation results, it is clarified that the proposed method shows better performance comparing to the conventional reinforcement learning without considering symbiosis.