Abstract The One-Warehouse Multi-Retailer (OWMR) system is the prototypical distribution
and inventory system. Many OWMR variants exist, eg demand in excess of supply may be …

This study conducts a comprehensive analysis of deep reinforcement learning (DRL)
algorithms applied to supply chain inventory management (SCIM), which consists of a …

This paper studies the applicability of a deep reinforcement learning approach to three
different multi-echelon inventory systems, with the objective of minimizing the holding and …

This work investigates the application of Multi-Agent Deep Reinforcement Learning
(MADRL) on decentralized inventory management problems with multiple echelons …

Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory
problems? Academic/practical relevance: Given that DRL has successfully been applied in …

Is Deep Reinforcement Learning (DRL) effective at solving inventory problems? Given that
DRL has successfully been applied in computer games and robotics, supply chain …

This paper leverages recent developments in reinforcement learning and deep learning to
solve the supply chain inventory management (SCIM) problem, a complex sequential …

Deep reinforcement learning (DRL) has shown great potential for sequential decision-
making, including early developments in inventory control. Yet, the abundance of choices …

This paper describes the application of reinforcement learning (RL) to multi-product
inventory management in supply chains. The problem description and solution are both …

Deep reinforcement learning (DRL) has proven to be an effective, general-purpose
technology to develop 'good'replenishment policies in inventory management. We show …