基于混合深度強(qiáng)化學(xué)習(xí)的云制造云邊協(xié)同聯(lián)合卸載策略

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Joint offloading strategy for cloud manufacturing based on hybrid deep reinforcement learning in cloud-edge collaboration

Zhang Yaru，Guo Yinzhang? （Colegeof ComputerScience&Technology，Taiyuan UniversityofScience&Technology，TaiyuanO3o024，China）

Abstract：Toaddress theissueofreal-time perceptiondata frommanufacturingresources being dificult toprocess promptlyin acloud-edgecollaborative cloud manufacturingenvironment，considering uncertainfactors suchas the limited computingresources attheedge，dynamicallychanging network conditions，andtaskloads，thispaper proposedacloud-edgecolaborative jointoffloading strategybasedonmixed-baseddeepreinforcementlearning（M-DRL）.Firstly，thisstrategyestablishedajoint ffloadigmodelbycombiningdiscretemodeloffloadinginthecloudwithcontinuoustaskoffloadingattheedge.Secondly， this strategydefinedtheoptimizationproblemasaMDPtominimizethetotalcostofdelayandenergyconsumptionoveraperiod. Finally，thispaper used the M -DRL algorithm，which utilized an integrated exploration strategy of DDPG and DQNand introducedalongshort-termmemory network（LSTM）intothenetworkarchitecture，tosolvethisoptimizationproblem.Simulation results showthatcompared with some existing ofloadingalgorithms，the M-DRL method has goodconvergence and Stability， andsignificantlyreduces thetotalsystemcost.Itprovidesanefectivesolutionforthetimelyprocessingofmanufacturingresource perception data.

Keywords：cloudmanufacturing；cloud-edgecolaboration;jointofloading；LSTMreinforcementlearning；Markovdecision process（MDP）

0 引言

云制造作為一種新興的生產(chǎn)模式，以前所未有的速度改變著全球制造業(yè)的格局。（剩余20442字）