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In this paper, we consider a novel Three Hierarchical decomposition approach for Multi-Player Pursuit Evaders (MPPE) game. In multi-player pursuit evasion game, hierarchical framework is applied widely in order to decompose the original complicated multi-player game into multiple small scale games. In this paper, we first study the number of pursuers which necessitates; the capture condition and the time of all evaders have been captured. Then, describe the Distributed Task Assignment Stage Based on dynamic Coalition Formation. Last, a novel multi-agent Q-learning approach based on Evolutionary Game Theoretic model is used for pursue. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed Hierarchical architecture and learning approach.