Structure-Informed Deep Reinforcement Learning for Inventory Management

2025-07-29

AI Summary

This paper addresses the practical deployment challenges of deep reinforcement learning (DRL) in inventory management by proposing a structured DRL framework that jointly ensures interpretability and robustness. Methodologically: (1) it designs a structured prior policy network that explicitly encodes analytical properties of classical operations research policies—such as the newsvendor solution and dual-sourcing threshold structures—into the neural architecture; (2) it employs the DirectBackprop algorithm to enable cross-product policy learning directly from historical demand data, bypassing distributional assumptions. Contributions include: significantly improved out-of-sample generalization and policy interpretability; superior or competitive performance against optimal analytical policies and heuristics across complex settings—including multi-period, perishable-item, and dual-sourcing inventory systems; minimal hyperparameter tuning; and robustness under non-stationary demand environments.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper investigates the application of Deep Reinforcement Learning (DRL) to classical inventory management problems, with a focus on practical implementation considerations. We apply a DRL algorithm based on DirectBackprop to several fundamental inventory management scenarios including multi-period systems with lost sales (with and without lead times), perishable inventory management, dual sourcing, and joint inventory procurement and removal. The DRL approach learns policies across products using only historical information that would be available in practice, avoiding unrealistic assumptions about demand distributions or access to distribution parameters. We demonstrate that our generic DRL implementation performs competitively against or outperforms established benchmarks and heuristics across these diverse settings, while requiring minimal parameter tuning. Through examination of the learned policies, we show that the DRL approach naturally captures many known structural properties of optimal policies derived from traditional operations research methods. To further improve policy performance and interpretability, we propose a Structure-Informed Policy Network technique that explicitly incorporates analytically-derived characteristics of optimal policies into the learning process. This approach can help interpretability and add robustness to the policy in out-of-sample performance, as we demonstrate in an example with realistic demand data. Finally, we provide an illustrative application of DRL in a non-stationary setting. Our work bridges the gap between data-driven learning and analytical insights in inventory management while maintaining practical applicability.

Problem

• Applying DRL to solve inventory management problems practically
• Learning policies using historical data without unrealistic assumptions
• Improving policy performance with Structure-Informed Policy Network

Innovation

• Uses DirectBackprop DRL for inventory scenarios
• Learns policies from historical data only
• Incorporates Structure-Informed Policy Network technique