  In this study, we analyze and compare the performance of state-of-the-art
deep reinforcement learning algorithms for solving the supply chain inventory
management problem. This complex sequential decision-making problem consists of
determining the optimal quantity of products to be produced and shipped across
different warehouses over a given time horizon. In particular, we present a
mathematical formulation of a two-echelon supply chain environment with
stochastic and seasonal demand, which allows managing an arbitrary number of
warehouses and product types. Through a rich set of numerical experiments, we
compare the performance of different deep reinforcement learning algorithms
under various supply chain structures, topologies, demands, capacities, and
costs. The results of the experimental plan indicate that deep reinforcement
learning algorithms outperform traditional inventory management strategies,
such as the static (s, Q)-policy. Furthermore, this study provides detailed
insight into the design and development of an open-source software library that
provides a customizable environment for solving the supply chain inventory
management problem using a wide range of data-driven approaches.
