  When deploying autonomous agents in the real world, we need effective ways of
communicating objectives to them. Traditional skill learning has revolved
around reinforcement and imitation learning, each with rigid constraints on the
format of information exchanged between the human and the agent. While scalar
rewards carry little information, demonstrations require significant effort to
provide and may carry more information than is necessary. Furthermore, rewards
and demonstrations are often defined and collected before training begins, when
the human is most uncertain about what information would help the agent. In
contrast, when humans communicate objectives with each other, they make use of
a large vocabulary of informative behaviors, including non-verbal
communication, and often communicate throughout learning, responding to
observed behavior. In this way, humans communicate intent with minimal effort.
In this paper, we propose such interactive learning as an alternative to reward
or demonstration-driven learning. To accomplish this, we introduce a
multi-agent training framework that enables an agent to learn from another
agent who knows the current task. Through a series of experiments, we
demonstrate the emergence of a variety of interactive learning behaviors,
including information-sharing, information-seeking, and question-answering.
Most importantly, we find that our approach produces an agent that is capable
of learning interactively from a human user, without a set of explicit
demonstrations or a reward function, and achieving significantly better
performance cooperatively with a human than a human performing the task alone.
