# Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chains

arXiv: https://arxiv.org/abs/2410.18631 · HTML: https://mlsanity.com/paper/2410.18631/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2024.

## Built on by (most cited first, 10 of 10)

- [Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management](https://mlsanity.com/paper/2605.17036/) (2026)
- [A Theoretical Framework for Graph-based Digital Twins for Supply Chain Management and Optimization](https://mlsanity.com/paper/2504.03692/) (2025)
- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization](https://mlsanity.com/paper/2503.18201/) (2025)
- [gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods](https://mlsanity.com/paper/2605.11355/) (2026)
- [MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization](https://mlsanity.com/paper/2509.06490/) (2025)
- [Local-Canonicalization Equivariant Graph Neural Networks for Sample-Efficient and Generalizable Swarm Robot Control](https://mlsanity.com/paper/2509.14431/) (2025)
- [SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation](https://mlsanity.com/paper/2601.14566/) (2026)
- [SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation](https://mlsanity.com/paper/2606.24694/) (2026)
- [GPEvac: GNN-Based PPO for Adaptive Evacuation Routing During Shooting Events](https://mlsanity.com/paper/2609.16163/) (2026)

## Abstract (arXiv)

Inventory control in modern supply chains has attracted significant attention due to the increasing number of disruptive shocks and the challenges posed by complex dynamics, uncertainties, and limited collaboration. Traditional methods, which often rely on static parameters, struggle to adapt to changing environments. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework with Graph Neural Networks (GNNs) for state representation to address these limitations. Our approach redefines the action space by parameterizing heuristic inventory control policies, making it adaptive as the parameters dynamically adjust based on system conditions. By leveraging the inherent graph structure of supply chains, our framework enables agents to learn the system's topology, and we employ a centralized learning, decentralized execution scheme that allows agents to learn collaboratively while overcoming information-sharing constraints. Additionally, we incorporate global mean pooling and regularization techniques to enhance performance. We test the capabilities of our proposed approach on four different supply chain configurations and conduct a sensitivity analysis. This work paves the way for utilizing MARL-GNN frameworks to improve inventory management in complex, decentralized supply chain environments.