Being able to harness the power of large, static datasets for developing autonomous multi-agent systems could unlock enormous value for real-world applications. Many important industrial systems are multi-agent in nature and are difficult to model using bespoke simulators. However, in industry, distributed system processes can often be recorded during operation, and large quantities of demonstrative data can be stored. Offline multi-agent reinforcement learning (MARL) provides a promising paradigm for building effective online controllers from static datasets. However, offline MARL is still in its infancy, and, therefore, lacks standardised benchmarks, baselines and evaluation protocols typically found in more mature subfields of RL. This deficiency makes it difficult for the community to sensibly measure progress. In this work, we aim to fill this gap by releasing \emph{off-the-grid MARL (OG-MARL)}: a framework for generating offline MARL datasets and algorithms. We release an initial set of datasets and baselines for cooperative offline MARL, created using the framework, along with a standardised evaluation protocol. Our datasets provide settings that are characteristic of real-world systems, including complex dynamics, non-stationarity, partial observability, suboptimality and sparse rewards, and are generated from popular online MARL benchmarks. We hope that OG-MARL will serve the community and help steer progress in offline MARL, while also providing an easy entry point for researchers new to the field.

该研究为填补离线多智能体强化学习（MARL）领域中缺乏标准基准和评估方法的空白，提出了一个名为OG-MARL的离线MARL数据集和算法框架，包括一套标准评估方案。OG-MARL的数据集本质上是从在线MARL基准中生成的，具有复杂的动态、非静态性、局部可观察性、次优性和稀疏奖励等特征。

离网 MARL: 一个数据集生成框架，具备合作离线多智能体强化学习基线