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This article discusses using deep reinforcement learning to optimize inventory levels in the beer game supply chain simulation. The beer game is commonly used to demonstrate the bullwhip effect caused by a lack of coordination in decentralized supply chains. The authors develop a deep Q-network algorithm that can play the role of any agent in the beer game. Through training, the algorithm learns optimal ordering behavior even when other agents do not follow rational policies. Once trained, it can execute in

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ruoruo00902000

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