mlsanityData to 14 Oct 2026

A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

arXiv 2306.07542 ↗First posted 13 Jun 2023cs.AIcited by 10

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Abstract (arXiv)

Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. This paradigm is applicable to various industrial scenarios such as autonomous driving, quantitative trading, and inventory management. However, applying MARL to these real-world scenarios is impeded by many challenges such as scaling up, complex agent interactions, and non-stationary dynamics. To incentivize the research of MARL on these challenges, we develop MABIM (Multi-Agent Benchmark for Inventory Management) which is a multi-echelon, multi-commodity inventory management simulator that can generate versatile tasks with these different challenging properties. Based on MABIM, we evaluate the performance of classic operations research (OR) methods and popular MARL algorithms on these challenging tasks to highlight their weaknesses and potential.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 13 Jun 2023.

Builds on

26 of 54 references.

References · 26

  1. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  3. 2019The StarCraft Multi-Agent Challengecited by 597
  4. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  5. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  6. 2017Robust Adversarial Reinforcement Learningcited by 451
  7. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  8. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
  9. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  10. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  11. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  12. 2021ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrationscited by 197
14 more
  1. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  2. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 177
  3. 2020Qlib: An AI-oriented Quantitative Investment Platformcited by 81
  4. 2020Towards Playing Full MOBA Games with Deep Reinforcement Learningcited by 78
  5. 2018RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertisingcited by 65
  6. 2018Generative Adversarial User Model for Reinforcement Learning Based Recommendation Systemcited by 64
  7. 2020Reinforcement Learning in Economics and Financecited by 58
  8. 2020Multi-agent Reinforcement Learning for Networked System Controlcited by 53
  9. 2020OR-Gym: A Reinforcement Learning Library for Operations Research Problemscited by 51
  10. 2021Collective Intelligence for Deep Learning: A Survey of Recent Developmentscited by 34
  11. 2018StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learningcited by 25
  12. 2018Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learningcited by 24
  13. 2022Multi-Agent Reinforcement Learning with Shared Resources for Inventory Managementcited by 5
  14. 2023Towards Generalizable Reinforcement Learning for Trade Executioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 4.

10 papers.

  1. 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agentscited by 50
  2. 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reductioncited by 24
  3. 2024Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seekingcited by 16
  4. 2025Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learningcited by 7
  5. 2024Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learningcited by 3
  6. 2025Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimizationcited by 1
  7. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 1
  8. 2026gym-invmgmt: An Open Benchmarking Framework for Inventory Management Methodscited by 1
  9. 2026SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptionscited by 0
  10. 2026Who Bears the Burden? Learning Responsibility for Shared Constraints in Multi-Agent Reinforcement Learningcited by 0