论文标题
具有共享资源用于库存管理的多机构增强学习
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
论文作者
论文摘要
在本文中，我们考虑了库存管理（IM）问题，我们需要为大量库存保存单位（SKU）做出补充决策以平衡其供需。在我们的环境中，对共享资源的约束（例如库存能力）伴随着每个SKU的原本独立控制。我们用共享资源的随机游戏（SRSG）提出了该结构的问题，并提出了一种称为上下文感知的分散PPO（CD-PPO）的有效算法。通过广泛的实验，我们证明了与标准MARL算法相比，CD-PPO可以加速学习程序。
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their supply and demand. In our setting, the constraint on the shared resources (such as the inventory capacity) couples the otherwise independent control for each SKU. We formulate the problem with this structure as Shared-Resource Stochastic Game (SRSG)and propose an efficient algorithm called Context-aware Decentralized PPO (CD-PPO). Through extensive experiments, we demonstrate that CD-PPO can accelerate the learning procedure compared with standard MARL algorithms.
