MARLIM: Multi-Agent Reinforcement Learning for Inventory Management
Rémi Leluc Elie Kadoche Antoine Bertoncello Sébastien Gourvénec
Maintaining a balance between the supply and demand of products by optimizing
replenishment decisions is one of the most important challenges in the supply
chain industry. This paper presents a novel reinforcement learning framework
called MARLIM, to address the inventory management problem for a single-echelon
multi-products supply chain with stochastic demands and lead-times. Within this
context, controllers are developed through single or multiple agents in a
cooperative setting. Numerical experiments on real data demonstrate the
benefits of reinforcement learning methods over traditional baselines.