Cascading Multi-Agent Policy Optimization for Demand Forecasting

Saeed Varasteh Yazdi ()
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Saeed Varasteh Yazdi: EM - EMLyon Business School

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Abstract: Reliable demand forecasting is crucial for effective supply chain management, where inaccurate forecasts can lead to frequent out-of-stock or overstock situations. While numerous statistical and machine learning methods have been explored for demand forecasting, reinforcement learning approaches, despite their significant potential, remain little known in this domain. In this paper, we propose a multi-agent deep reinforcement learning solution designed to accurately predict demand across multiple stores. We present empirical evidence that demonstrates the effectiveness of our model using a real-world dataset. The results confirm the practicality of our proposed approach and highlight its potential to improve demand forecasting in retail and potentially other forecasting scenarios.

Keywords: reinforcement learning; multi-agent systems; demand forecasting (search for similar items in EconPapers)
Date: 2025-07-16
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Published in International Conference on Time Series and Forecasting 11th, Jul 2025, Canaria, Spain. pp.-17, ⟨10.3390/cmsf2025011018⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05656779

DOI: 10.3390/cmsf2025011018

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