# Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains

arXiv: https://arxiv.org/abs/2006.04037 · HTML: https://mlsanity.com/paper/2006.04037/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 13 of 13)

- [Deep Policy Iteration with Integer Programming for Inventory Management](https://mlsanity.com/paper/2112.02215/) (2021)
- [MARLIM: Multi-Agent Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2308.01649/) (2023)
- [Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management](https://mlsanity.com/paper/2212.07684/) (2022)
- [Agentic AI Framework for Smart Inventory Replenishment](https://mlsanity.com/paper/2511.23366/) (2025)
- [Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control](https://mlsanity.com/paper/2601.00121/) (2025)
- [Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management](https://mlsanity.com/paper/2303.10382/) (2023)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [Cooperative Multi-Agent Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2304.08769/) (2023)
- [Reinforcement Learning with Intrinsically Motivated Feedback Graph for Lost-sales Inventory Control](https://mlsanity.com/paper/2406.18351/) (2024)
- [Learning General Inventory Management Policy for Large Supply Chain Network](https://mlsanity.com/paper/2204.13378/) (2022)
- [Using General Value Functions to Learn Domain-Backed Inventory Management Policies](https://mlsanity.com/paper/2311.02125/) (2023)
- [Agent based modelling for continuously varying supply chains](https://mlsanity.com/paper/2312.15502/) (2023)
- [Hybrid Reinforcement Learning Framework for Mixed-Variable Problems](https://mlsanity.com/paper/2405.20500/) (2024)

## Abstract (arXiv)

This paper describes the application of reinforcement learning (RL) to multi-product inventory management in supply chains. The problem description and solution are both adapted from a real-world business solution. The novelty of this problem with respect to supply chain literature is (i) we consider concurrent inventory management of a large number (50 to 1000) of products with shared capacity, (ii) we consider a multi-node supply chain consisting of a warehouse which supplies three stores, (iii) the warehouse, stores, and transportation from warehouse to stores have finite capacities, (iv) warehouse and store replenishment happen at different time scales and with realistic time lags, and (v) demand for products at the stores is stochastic. We describe a novel formulation in a multi-agent (hierarchical) reinforcement learning framework that can be used for parallelised decision-making, and use the advantage actor critic (A2C) algorithm with quantised action spaces to solve the problem. Experiments show that the proposed approach is able to handle a multi-objective reward comprised of maximising product sales and minimising wastage of perishable products.