A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao +2 more
6/13/2023
cs.AI

No official code found. Best implementation: VictorYXL/ReplenishmentEnv (49 stars, Apache-2.0, last commit 2024-04-16).

Abstract

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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Code Implementations(1)

VictorYXL/ReplenishmentEnvCommunity100%
Apache-2.0
4913Shell, PythonNov 30, 20222 years agoApache-2.0

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Cite this paper

@article{yang2023versatile,
  title  = {A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management},
  author = {Xianliang Yang and Zhihao Liu and Wei Jiang and Chuheng Zhang and Li Zhao and Lei Song and Jiang Bian},
  year   = {2023},
  eprint = {2306.07542},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.07542v1}
}

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