  Due to the complex interactions between agents, learning multi-agent control
policy often requires a prohibited amount of data. This paper aims to enable
multi-agent systems to effectively utilize past memories to adapt to novel
collaborative tasks in a data-efficient fashion. We propose the Multi-Agent
Coordination Skill Database, a repository for storing a collection of
coordinated behaviors associated with key vectors distinctive to them. Our
Transformer-based skill encoder effectively captures spatio-temporal
interactions that contribute to coordination and provides a unique skill
representation for each coordinated behavior. By leveraging only a small number
of demonstrations of the target task, the database enables us to train the
policy using a dataset augmented with the retrieved demonstrations.
Experimental evaluations demonstrate that our method achieves a significantly
higher success rate in push manipulation tasks compared with baseline methods
like few-shot imitation learning. Furthermore, we validate the effectiveness of
our retrieve-and-learn framework in a real environment using a team of wheeled
robots.
