  Few-shot learning is challenging for learning algorithms that learn each task
in isolation and from scratch. In contrast, meta-learning learns from many
related tasks a meta-learner that can learn a new task more accurately and
faster with fewer examples, where the choice of meta-learners is crucial. In
this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner
that can initialize and adapt any differentiable learner in just one step, on
both supervised learning and reinforcement learning. Compared to the popular
meta-learner LSTM, Meta-SGD is conceptually simpler, easier to implement, and
can be learned more efficiently. Compared to the latest meta-learner MAML,
Meta-SGD has a much higher capacity by learning to learn not just the learner
initialization, but also the learner update direction and learning rate, all in
a single meta-learning process. Meta-SGD shows highly competitive performance
for few-shot learning on regression, classification, and reinforcement
learning.
