Abstract：Aiming at the large statespace caused by the slow convergence of Q learning, a kind of multiagent cooperative learning is proposed by the coordination of boundary samples. Each agent is specialized in its subspace, and the agents coordinate through Boolean functions in boundary states. Simulation results have proved that the proposed method performs better than the traditional global learning.
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2008, Vol. 21 
