Expertise Based Cooperative Reinforcement Learning Methods (ECRLM) for Dynamic Decision Making in Retail Shop Application

Abstract

A novel approach for dynamic decision making in retail application by expertise based cooperative reinforcement learning methods (ECRLM) is proposed in this paper. Different cooperation schemes for cooperative reinforcement learning i.e. EGroup scheme, EDynamic scheme, EGoal-oriented scheme proposed here. Implementation outcome includes demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieves excellent action policies. This approach is developed for a three retailer shops in the retail market. Retailers be able to help with each other and can obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The retailers are the knowledge agents in the hypothesis and employ reinforcement learning to learn cooperatively in situation. Assuming significant hypothesis on the dealer’s stock policy, refill period, and arrival process of the consumers, the approach is modeled as Markov decision process model thus making it possible to apply learning algorithms.