  Team adaptation to new cooperative tasks is a hallmark of human intelligence,
which has yet to be fully realized in learning agents. Previous work on
multi-agent transfer learning accommodate teams of different sizes, heavily
relying on the generalization ability of neural networks for adapting to unseen
tasks. We believe that the relationship among tasks provides the key
information for policy adaptation. In this paper, we try to discover and
exploit common structures among tasks for more efficient transfer, and propose
to learn effect-based task representations as a common space of tasks, using an
alternatively fixed training scheme. We demonstrate that the task
representation can capture the relationship among tasks, and can generalize to
unseen tasks. As a result, the proposed method can help transfer learned
cooperation knowledge to new tasks after training on a few source tasks. We
also find that fine-tuning the transferred policies help solve tasks that are
hard to learn from scratch.
