  Model-agnostic meta-learners aim to acquire meta-learned parameters from
similar tasks to adapt to novel tasks from the same distribution with few
gradient updates. With the flexibility in the choice of models, those
frameworks demonstrate appealing performance on a variety of domains such as
few-shot image classification and reinforcement learning. However, one
important limitation of such frameworks is that they seek a common
initialization shared across the entire task distribution, substantially
limiting the diversity of the task distributions that they are able to learn
from. In this paper, we augment MAML with the capability to identify the mode
of tasks sampled from a multimodal task distribution and adapt quickly through
gradient updates. Specifically, we propose a multimodal MAML (MMAML) framework,
which is able to modulate its meta-learned prior parameters according to the
identified mode, allowing more efficient fast adaptation. We evaluate the
proposed model on a diverse set of few-shot learning tasks, including
regression, image classification, and reinforcement learning. The results not
only demonstrate the effectiveness of our model in modulating the meta-learned
prior in response to the characteristics of tasks but also show that training
on a multimodal distribution can produce an improvement over unimodal training.
