协同多智能体联合行动学习算法在零售商店决策中的应用

International journal of agent technologies and systems Pub Date : 2017-01-01 DOI:10.4018/IJATS.2017010101

D. Vidhate

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引用次数: 0

摘要

Thisarticlegivesanovelapproachtocooperativedecision-makingalgorithmsbyJoint Actionlearningfortheretailshopapplication。Accordingly，thisapproachpresents三个零售商店在retailmarketplace。Retailers canhelp到eachother和canobtainprofitfromcooperationknowledgethroughlearningtheirownstrategies thatjuststandfortheiraimsandbenefit。Thevendorsaretheknowledgeableagents toemploycooperativelearningtotraininthecircumstances。Assumingasignificant hypothesison thevendor的stockpolicy, restockperiod, andarrivalprocessof的消费者，theapproachwasformedasaMarkovmodel。Theproposedalgorithms learndynamicconsumerperformance。Moreover，thearticleillustratestheresultsof cooperativereinforcementlearningalgorithmsbyjointactionlearningofthreeshop agentsfortheperiodofone-yearsaleduration。Twoapproacheshavebeencompared inthearticle，i.e.multi-agentQLearningandjointactionlearning。关键词:消费者行为，合作学习，联合行动学习，强化学习

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

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

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International journal of agent technologies and systems

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