Abstract
A group of cooperative and homogeneous Q-learning agents can cooperate to learn faster and gain more knowledge. In order to do so, each learner agent must be able to evaluate the expertness and the intelligence level of the other agents, and to assess the knowledge and the information it gets from them. In addition, the learner needs a suitable method to properly combine its own knowledge and what it gains from the other agents according to their relative expertness. In this paper, some expertness measuring criteria are introduced. Also, a new cooperative learning method called weighted strategy sharing (WSS) is introduced. In WSS, based on the amount of its teammate expertness, each agent assigns a weight to their knowledge and utilizes it accordingly. WSS and the expertness criteria are tested on two simulated hunter–prey and object-pushing systems.