Cooperative Multi-Agent Joint Action Learning Algorithm (CMJAL) for Decision Making in Retail Shop Application

Abstract

Thisarticlegivesanovelapproachtocooperativedecision-makingalgorithmsbyJoint Actionlearningfortheretailshopapplication.Accordingly,thisapproachpresents three retailer stores in the retailmarketplace.Retailers canhelp to eachother and canobtainprofitfromcooperationknowledgethroughlearningtheirownstrategies thatjuststandfortheiraimsandbenefit.Thevendorsaretheknowledgeableagents toemploycooperativelearningtotraininthecircumstances.Assumingasignificant hypothesison thevendor’s stockpolicy, restockperiod, andarrivalprocessof the consumers,theapproachwasformedasaMarkovmodel.Theproposedalgorithms learndynamicconsumerperformance.Moreover,thearticleillustratestheresultsof cooperativereinforcementlearningalgorithmsbyjointactionlearningofthreeshop agentsfortheperiodofone-yearsaleduration.Twoapproacheshavebeencompared inthearticle,i.e.multi-agentQLearningandjointactionlearning. KeywoRDS Consumer Behavior, Cooperative Learning, Joint Action Learning, Reinforcement Learning