  Offline reinforcement learning leverages previously-collected offline
datasets to learn optimal policies with no necessity to access the real
environment. Such a paradigm is also desirable for multi-agent reinforcement
learning (MARL) tasks, given the increased interactions among agents and with
the enviroment. Yet, in MARL, the paradigm of offline pre-training with online
fine-tuning has not been studied, nor datasets or benchmarks for offline MARL
research are available. In this paper, we facilitate the research by providing
large-scale datasets, and use them to examine the usage of the Decision
Transformer in the context of MARL. We investigate the generalisation of MARL
offline pre-training in the following three aspects: 1) between single agents
and multiple agents, 2) from offline pretraining to the online fine-tuning, and
3) to that of multiple downstream tasks with few-shot and zero-shot
capabilities. We start by introducing the first offline MARL dataset with
diverse quality levels based on the StarCraftII environment, and then propose
the novel architecture of multi-agent decision transformer (MADT) for effective
offline learning. MADT leverages transformer's modelling ability of sequence
modelling and integrates it seamlessly with both offline and online MARL tasks.
A crucial benefit of MADT is that it learns generalisable policies that can
transfer between different types of agents under different task scenarios. On
StarCraft II offline dataset, MADT outperforms the state-of-the-art offline RL
baselines. When applied to online tasks, the pre-trained MADT significantly
improves sample efficiency, and enjoys strong performance both few-short and
zero-shot cases. To our best knowledge, this is the first work that studies and
demonstrates the effectiveness of offline pre-trained models in terms of sample
efficiency and generalisability enhancements in MARL.
