  We propose the novel few-shot teamwork (FST) problem, where skilled agents
trained in a team to complete one task are combined with skilled agents from
different tasks, and together must learn to adapt to an unseen but related
task. We discuss how the FST problem can be seen as addressing two separate
problems: one of reducing the experience required to train a team of agents to
complete a complex task; and one of collaborating with unfamiliar teammates to
complete a new task. Progress towards solving FST could lead to progress in
both multi-agent reinforcement learning and ad hoc teamwork.
