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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Coordinated Rule Acquisition of Decision Making on Supply Chain by Exploitation-Oriented Reinforcement Learning
-Beer Game as an Example-
verfasst von : Fumiaki Saitoh, Akihide Utani
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2013
Verlag: Springer Berlin Heidelberg
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Product order decision-making is an important feature of inventory control in supply chains. The beer game represents a typical task in this process. Recent approaches that have applied the agent model to the beer game have shown. Q-learning performing better than genetic algorithm (GA). However, flexibly adapting to dynamic environment is difficult for these approaches because their learning algorithm assume a static environment. As exploitation-oriented reinforcement learning algorithm are robust in dynamic environments, this study, approaches the beer game using profit sharing, a typical exploitation-oriented agent learning algorithm, and verifies its result’s validity by comparing performances.