MARLIM: Multi-Agent Reinforcement Learning for Inventory Management
Not on the map. Why?
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 67 references that are in the map. First posted on arXiv 3 Aug 2023.
Builds on
6 of 67 references.
References · 6
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
- 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 224
- 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 189
- 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 122
- 2020Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chainscited by 13
Built on by
Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2026RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environmentscited by 6
- 2025A Survey of Reinforcement Learning for Optimization in Automationcited by 4
- 2026RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environmentscited by 1
- 2026gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methodscited by 1
- 2025Learning Multi-Timescale Interventions under Safety and Resource Constraintscited by 0
- 2026RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments14 Jun 2026
- 2026gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methods12 May 2026
- 2026RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments17 Mar 2026
- 2025Learning Multi-Timescale Interventions under Safety and Resource Constraints5 Aug 2025
- 2025A Survey of Reinforcement Learning for Optimization in Automation13 Feb 2025