# Structure-Informed Deep Reinforcement Learning for Inventory Management

arXiv: https://arxiv.org/abs/2507.22040 · HTML: https://mlsanity.com/paper/2507.22040/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- First posted on arXiv 29 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Built on by (most cited first, 7 of 7)

- [DeepStock: Reinforcement Learning with Policy Regularizations for Inventory Management](https://mlsanity.com/paper/2603.19621/) (2026)
- [Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets](https://mlsanity.com/paper/2606.14679/) (2026)
- [Learning Multi-Timescale Interventions under Safety and Resource Constraints](https://mlsanity.com/paper/2508.03875/) (2025)
- [Deep Learning for Perishable Inventory Systems with Human Knowledge](https://mlsanity.com/paper/2601.15589/) (2026)
- [COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management](https://mlsanity.com/paper/2603.02396/) (2026)
- [Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations](https://mlsanity.com/paper/2609.03860/) (2026)
- [Proximal Residual Value Functions for Consistent Planning and Real-Time Execution](https://mlsanity.com/paper/2609.23242/) (2026)

## Abstract (arXiv)

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.