  Humans can quickly adapt to new partners in collaborative tasks (e.g. playing
basketball), because they understand which fundamental skills of the task (e.g.
how to dribble, how to shoot) carry over across new partners. Humans can also
quickly adapt to similar tasks with the same partners by carrying over
conventions that they have developed (e.g. raising hand signals pass the ball),
without learning to coordinate from scratch. To collaborate seamlessly with
humans, AI agents should adapt quickly to new partners and new tasks as well.
However, current approaches have not attempted to distinguish between the
complexities intrinsic to a task and the conventions used by a partner, and
more generally there has been little focus on leveraging conventions for
adapting to new settings. In this work, we propose a learning framework that
teases apart rule-dependent representation from convention-dependent
representation in a principled way. We show that, under some assumptions, our
rule-dependent representation is a sufficient statistic of the distribution
over best-response strategies across partners. Using this separation of
representations, our agents are able to adapt quickly to new partners, and to
coordinate with old partners on new tasks in a zero-shot manner. We
experimentally validate our approach on three collaborative tasks varying in
complexity: a contextual multi-armed bandit, a block placing task, and the card
game Hanabi.
