The paper gives novel approach Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail application. Furthermore, it put up different cooperation schemes for multiagent cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, EGoal driven and Expert agents scheme. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speedup the collection of agents that achieve excellent action policies. Accordingly this approach presents three retailer stores in the retail market place. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train in the circumstances. Assuming significant hypothesis on the vendor's stock policy, restock period, arrival process of the consumers, the approach is formed as Markov decision process model that makes it possible to design learning algorithms. The proposed algorithms noticeably learn dynamic consumer performance. Moreover, the paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for the period of one year sale duration. The results obtained by the proposed expert agent based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.
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