Problem Definition: Are traditional deep reinforcement learning (DRL) algorithms, developed
for a broad range of purposes including game-play and robotics, the most suitable machine …

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

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

Inventory management offers unique opportunities for reliably evaluating and applying deep
reinforcement learning (DRL). Rather than evaluate DRL algorithms by comparing against …

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

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

Deep reinforcement learning (DRL) effectively solves complex inventory problems with a
multi-dimensional state space. However, most approaches use a discrete action …

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

The boom of the e-commerce industry in recent years prompts the focus of inventory
management into large-scale problems with multiple products and multi-echelon supply …