  Imitation learning allows agents to learn complex behaviors from
demonstrations. However, learning a complex vision-based task may require an
impractical number of demonstrations. Meta-imitation learning is a promising
approach towards enabling agents to learn a new task from one or a few
demonstrations by leveraging experience from learning similar tasks. In the
presence of task ambiguity or unobserved dynamics, demonstrations alone may not
provide enough information; an agent must also try the task to successfully
infer a policy. In this work, we propose a method that can learn to learn from
both demonstrations and trial-and-error experience with sparse reward feedback.
In comparison to meta-imitation, this approach enables the agent to effectively
and efficiently improve itself autonomously beyond the demonstration data. In
comparison to meta-reinforcement learning, we can scale to substantially
broader distributions of tasks, as the demonstration reduces the burden of
exploration. Our experiments show that our method significantly outperforms
prior approaches on a set of challenging, vision-based control tasks.
