# MARLIM: Multi-Agent Reinforcement Learning for Inventory Management

arXiv: https://arxiv.org/abs/2308.01649 · HTML: https://mlsanity.com/paper/2308.01649/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 67 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains](https://mlsanity.com/paper/2006.04037/) (2020)

## Built on by (most cited first, 6 of 6)

- [RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments](https://mlsanity.com/paper/2606.15862/) (2026)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments](https://mlsanity.com/paper/2603.16453/) (2026)
- [gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods](https://mlsanity.com/paper/2605.11355/) (2026)
- [Learning Multi-Timescale Interventions under Safety and Resource Constraints](https://mlsanity.com/paper/2508.03875/) (2025)
- [Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations](https://mlsanity.com/paper/2607.23434/) (2026)

## Abstract (arXiv)

Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learning framework called MARLIM, to address the inventory management problem for a single-echelon multi-products supply chain with stochastic demands and lead-times. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.