用双深度强化学习自动选预测模型，提升供应链需求预测韧性
Designing a double deep reinforcement learning selection tool for resilient demand prediction
- 设计双深度强化学习代理，动态选择最优预测模型
- 在生鲜与零食销售数据上表现优于现有方法，鲁棒性强
- 新早停策略加速训练，适合工业级自动化预测场景
人工智能在供应链预测中的应用已持续数十年，但选择合适预测方案仍具挑战，因数据集特性各异。尽管自上世纪八十年代已有相关研究，近年来需求预测的发展带来了新思路。本文提出一种新型双深度强化学习架构，作为预测委员会中的决策代理，在预测时自动选择最优模型。同时引入基于平均奖励收敛的新型早停机制，显著缩短训练时间。通过在生鲜销售和零食需求数据集上的实证研究验证，该方法在性能上优于当前主流方法，展现出良好的鲁棒性。
原文摘要 · Abstract (English)
The use of artificial intelligence in supply chain forecasting has attracted many scientific studies for several decades. However, the process of selecting an appropriate forecasting solution becomes a daunting task. This complexity arises due to the distinct features inherent to each dataset. Research to tackle this issue has been performed since the eighties but recent development of demand forecasting has opened new perspectives. This research aims to enhance automatic forecasting model selection by proposing a novel architecture that acts as a double deep reinforcement learning agent, selecting automatically a forecasting model from the forecasting committee at the time of prediction. Moreover, a novel early-stopping approach based on average reward convergence has been introduced to expedite training time. To evaluate the model's performance, an empirical study was conducted utilizing grocery sales datasets and snack demands datasets. The experimental results demonstrate the robustness of the proposed approach when compared to state-of-the-art methods.
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