  Developing agents that can quickly adapt their behavior to new tasks remains
a challenge. Meta-learning has been applied to this problem, but previous
methods require either specifying a reward function which can be tedious or
providing demonstrations which can be inefficient. In this paper, we
investigate if, and how, a "helper" agent can be trained to interactively adapt
their behavior to maximize the reward of another agent, whom we call the
"prime" agent, without observing their reward or receiving explicit
demonstrations. To this end, we propose to meta-learn a helper agent along with
a prime agent, who, during training, observes the reward function and serves as
a surrogate for a human prime. We introduce a distribution of multi-agent
cooperative foraging tasks, in which only the prime agent knows the objects
that should be collected. We demonstrate that, from the emerged physical
communication, the trained helper rapidly infers and collects the correct
objects.
