Deep reinforcement learning (DRL) has shown great potential for sequential decision-making, including early developments in inventory control. Yet, the abundance of choices that come with designing a DRL algorithm, combined with the intense computational effort to tune and evaluate each choice, may hamper their application in practice. This paper describes the key design choices of DRL algorithms to facilitate their implementation in inventory control. We also shed light on possible future research avenues that may elevate the current state-of-the-art of DRL applications for inventory control and broaden their scope by leveraging and improving on the structural policy insights within inventory research. Our discussion and roadmap may also spur future research in other domains within operations management.

深度强化学习 (DRL) 已显示出在顺序决策方面的巨大潜力，包括库存控制的早期发展。然而，设计 DRL 算法带来的大量选择，再加上调整和评估每个选择的大量计算工作，可能会阻碍它们在实践中的应用。本文描述了 DRL 算法的关键设计选择，以促进它们在库存控制中的实现。我们还阐明了未来可能的研究途径，这些途径可能会通过利用和改进库存研究中的结构性政策见解来提升当前最先进的 DRL 应用程序的库存控制并扩大其范围。我们的讨论和路线图也可能会刺激运营管理中其他领域的未来研究。