  Being able to harness the power of large datasets for developing cooperative
multi-agent controllers promises to 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 processes can often be recorded during operation, and large
quantities of demonstrative data stored. Offline multi-agent reinforcement
learning (MARL) provides a promising paradigm for building effective
decentralised controllers from such datasets. However, offline MARL is still in
its infancy and therefore lacks standardised benchmark datasets and baselines
typically found in more mature subfields of reinforcement learning (RL). These
deficiencies make it difficult for the community to sensibly measure progress.
In this work, we aim to fill this gap by releasing off-the-grid MARL (OG-MARL):
a growing repository of high-quality datasets with baselines for cooperative
offline MARL research. Our datasets provide settings that are characteristic of
real-world systems, including complex environment dynamics, heterogeneous
agents, non-stationarity, many agents, partial observability, suboptimality,
sparse rewards and demonstrated coordination. For each setting, we provide a
range of different dataset types (e.g. Good, Medium, Poor, and Replay) and
profile the composition of experiences for each dataset. We hope that OG-MARL
will serve the community as a reliable source of datasets and help drive
progress, while also providing an accessible entry point for researchers new to
the field.

Machine Learning;Artificial Intelligence;Multiagent Systems