Building a single generalist agent with zero-shot capability has recently
sparked significant advancements in decision-making. However, extending this
capability to multi-agent scenarios presents challenges. Most current works
struggle with zero-shot capabilities, due to two challenges particular to the
multi-agent settings: a mismatch between centralized pretraining and
decentralized execution, and varying agent numbers and action spaces, making it
difficult to create generalizable representations across diverse downstream
tasks. To overcome these challenges, we propose a \textbf{Mask}ed pretraining
framework for \textbf{M}ulti-\textbf{a}gent decision making (MaskMA). This
model, based on transformer architecture, employs a mask-based collaborative
learning strategy suited for decentralized execution with partial observation.
Moreover, MaskMA integrates a generalizable action representation by dividing
the action space into actions toward self-information and actions related to
other entities. This flexibility allows MaskMA to tackle tasks with varying
agent numbers and thus different action spaces. Extensive experiments in SMAC
reveal MaskMA, with a single model pretrained on 11 training maps, can achieve
an impressive 77.8% zero-shot win rate on 60 unseen test maps by decentralized
execution, while also performing effectively on other types of downstream tasks
(\textit{e.g.,} varied policies collaboration and ad hoc team play).

提出了一种基于 Transformer 架构的 MaskMA 预训练框架，通过分布式执行和部分观察的方式，同时解决了多智能体决策中分布式预训练和执行不匹配以及智能体数量和行动空间多样性的问题，实验结果显示 MaskMA 在多个任务上都表现出色，包括 77.8％的零 - shot 胜率和有效的策略协作和临时团队游戏能力。