Inventory management in warehouses directly affects profits made by
manufacturers. Particularly, large manufacturers produce a very large variety
of products that are handled by a significantly large number of retailers. In
such a case, the computational complexity of classical inventory management
algorithms is inordinately large. In recent years, learning-based approaches
have become popular for addressing such problems. However, previous studies
have not been managed systems where both the number of products and retailers
are large. This study proposes a reinforcement learning-based warehouse
inventory management algorithm that can be used for supply chain systems where
both the number of products and retailers are large. To solve the computational
problem of handling large systems, we provide a means of approximate simulation
of the system in the training phase. Our experiments on both real and
artificial data demonstrate that our algorithm with approximated simulation can
successfully handle large supply chain networks.

研究了基于强化学习的仓库库存管理算法，该算法可以用于产品和零售商数量都庞大的供应链系统，并提供了一种训练阶段中模拟系统的近似方法来解决处理大型系统的计算问题，实验证明了该算法可以成功地处理大型供应链网络。

大型供应链网络的通用库存管理策略学习

Learning General Inventory Management Policy for Large Supply Chain Network

In warehouses, specialized agents need to navigate, avoid obstacles and
maximize the use of space in the warehouse environment. Due to the
unpredictability of these environments, reinforcement learning approaches can
be applied to complete these tasks. In this paper, we propose using Deep
Reinforcement Learning (DRL) to address the robot navigation and obstacle
avoidance problem and traditional Q-learning with minor variations to maximize
the use of space for product placement. We first investigate the problem for
the single robot case. Next, based on the single robot model, we extend our
system to the multi-robot case. We use a strategic variation of Q-tables to
perform multi-agent Q-learning. We successfully test the performance of our
model in a 2D simulation environment for both the single and multi-robot cases.

提出采用深度强化学习和传统的 Q-learning 方法来解决仓库机器人导航，避障和产品摆放等问题，包括针对单机器人，多机器人的情况，成功在二维仿真环境中测试了模型的性能。