Deep reinforcement learning has been coined as a promising research avenue to solve sequential decision-making problems, especially if few is known about the optimal policy structure. We apply the proximal policy optimization algorithm to the intractable joint replenishment problem. We demonstrate how the algorithm approaches the optimal policy structure and outperforms two other heuristics. Its deployment in supply chain control towers can orchestrate and facilitate collaborative shipping in the Physical Internet.

中文翻译：

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最近政策优化在联合补给问题中的应用

深度强化学习已被认为是解决顺序决策问题的有前途的研究途径，尤其是在对最佳政策结构知之甚少的情况下。我们将近端策略优化算法应用于难解决的联合补给问题。我们演示了该算法如何优化策略结构并优于其他两种启发式算法。它在供应链控制塔中的部署可以协调并促进物理Internet中的协作运输。