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,