Deep Multi-Agent Reinforcement Learning using DNN-Weight Evolution to Optimize Supply Chain Performance

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dc.contributor.authorFuji, Taiki
dc.contributor.authorIto, Kiyoto
dc.contributor.authorMatsumoto, Kohsei
dc.contributor.authorYano, Kazuo
dc.date.accessioned2017-12-28T00:46:43Z
dc.date.available2017-12-28T00:46:43Z
dc.date.issued2018-01-03
dc.description.abstractTo develop a supply chain management (SCM) system that performs optimally for both each entity in the chain and the entire chain, a multi-agent reinforcement learning (MARL) technique has been developed. To solve two problems of the MARL for SCM (building a Markov decision processes for a supply chain and avoiding learning stagnation in a way similar to the "prisoner's dilemma"), a learning management method with deep-neural-network (DNN)-weight evolution (LM-DWE) has been developed. By using a beer distribution game (BDG) as an example of a supply chain, experiments with a four-agent system were performed. Consequently, the LM-DWE successfully solved the above two problems and achieved 80.0% lower total cost than expert players of the BDG.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.157
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50044
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntelligent Decision Support for Logistics and Supply Chain Management
dc.subjectDeep learning, Evolutionary computation, Multi-agent reinforcement learning, Multi-agent system, Supply chain management
dc.titleDeep Multi-Agent Reinforcement Learning using DNN-Weight Evolution to Optimize Supply Chain Performance
dc.typeConference Paper
dc.type.dcmiText

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Intelligent Decision Support for Logistics and Supply Chain Management