  Recent advances in one-shot learning have produced models that can learn from
a handful of labeled examples, for passive classification and regression tasks.
This paper combines reinforcement learning with one-shot learning, allowing the
model to decide, during classification, which examples are worth labeling. We
introduce a classification task in which a stream of images are presented and,
on each time step, a decision must be made to either predict a label or pay to
receive the correct label. We present a recurrent neural network based
action-value function, and demonstrate its ability to learn how and when to
request labels. Through the choice of reward function, the model can achieve a
higher prediction accuracy than a similar model on a purely supervised task, or
trade prediction accuracy for fewer label requests.
