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Cooperative Learning in a multi-agent system can improve the learning quality and learning speed. The improvement can be gained if each agent detects the expert agents and use their knowledge properly. In this paper, a new cooperative learning method, called Weighted Strategy Sharing (WSS) is introduced. Also some criteria are introduced to measure the expertness of agents. In WSS, based on the amount of its teammate expertness, each agent assigns a weight to their knowledge. These weights are used in sharing knowledge among agents in our system. WSS and the expertness criteria are tested on two simulated Hunter-Prey problem and Object Pushing systems.