Interpretable reinforcement learning with structured policies for prescriptive supply chain analytics.

Abstract

Reinforcement Learning (RL) offers significant potential for solving complex, sequential decision-making problems in supply chain management, such as dynamic inventory control. However, the adoption of state-of-the-art deep RL methods is severely hampered by their "black box" nature, as the resulting policies are notoriously opaque and difficult to trust in high-stakes industrial environments. To address this critical interpretability-performance gap, we introduce Structured Policy Reinforcement Learning (SPRL), a novel framework that embeds transparency directly into the agent's learning process. SPRL uses a decoupled architecture to distill value estimates from a powerful Q-learning agent into an inherently interpretable Decision Tree (DT). Crucially, the entire process is governed by a hard operational constraint (Inventory Position Cap, [Formula: see text]), which acts as an operational guardrail to stabilize training and enforce lean policies. We demonstrate that the optimized SPRL-DT policy achieves highly efficient combined performance, realizing a mean total cost of [Formula: see text] (a 45% reduction compared to the constrained DQN baseline) while maintaining an excellent service level of over 91%. This performance is achieved with a low complexity structure ([Formula: see text] nodes), offering a highly transparent and verifiable solution. Our results validate that SPRL enables the development of trustworthy autonomous systems, paving the way for the practical deployment of RL in real-world supply chain operations.