为离线多智能体强化学习建立统一数据标准，提升可复现性。
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
- 提出生成新数据集的清晰指南，确保方法一致
- 标准化80多个现有数据集，支持统一访问和分析
- 提供工具帮助理解数据特性，推动领域发展
离线多智能体强化学习（Offline MARL）依赖静态数据集来寻找多智能体系统的最优控制策略。尽管该领域本质上是数据驱动的，但以往研究普遍忽视数据本身，仅追求先进结果。我们通过文献调研发现，多数工作自行生成数据集，缺乏一致方法，且对数据特征描述不全。进一步分析表明，算法性能与所用数据集高度耦合，因此亟需统一实验基础。为此，本文提出三项关键贡献：(1) 新数据集生成的明确指南；(2) 对超过80个现有数据集进行标准化处理，托管于公开仓库，采用统一存储格式并提供易用API；(3) 一套数据分析工具，帮助深入理解数据特性，助力后续研究。这些工作共同推动离线MARL中的数据使用与数据意识提升。
原文摘要 · Abstract (English)
Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though the field is by definition data-driven, efforts have thus far neglected data in their drive to achieve state-of-the-art results. We first substantiate this claim by surveying the literature, showing how the majority of works generate their own datasets without consistent methodology and provide sparse information about the characteristics of these datasets. We then show why neglecting the nature of the data is problematic, through salient examples of how tightly algorithmic performance is coupled to the dataset used, necessitating a common foundation for experiments in the field. In response, we take a big step towards improving data usage and data awareness in offline MARL, with three key contributions: (1) a clear guideline for generating novel datasets; (2) a standardisation of over 80 existing datasets, hosted in a publicly available repository, using a consistent storage format and easy-to-use API; and (3) a suite of analysis tools that allow us to understand these datasets better, aiding further development.
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