  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.
