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A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

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arxiv 2306.07542 v1 pith:YAKOOMKQ submitted 2023-06-13 cs.AI

Xianliang Yang , Zhihao Liu , Wei Jiang , Chuheng Zhang , Li Zhao , Lei Song , Jiang Bian This is my paper · ORCID
classification cs.AI
keywords inventorymanagementmarlmulti-agentbenchmarkchallengeschallenginglearning
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

    cs.AI 2025-07 conditional novelty 6.0 of 10

    A survey proposing adaptability as a three-part taxonomy (learning, policy, scenario-driven) for organizing and evaluating MARL under changing conditions.

  2. Collaborating in a competitive world: Heterogeneous Multi-Agent Decision Making in Symbiotic Supply Chain Environments

    cs.MA 2025-01 conditional novelty 5.0 of 10

    Separate per-node policies reduce the bullwhip effect in a simulated two-node supply chain, but a single shared policy earns more in low-demand settings; SAC beats PPO in high demand.

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