  One of the major challenges in training deep architectures for predictive
tasks is the scarcity and cost of labeled training data. Active Learning (AL)
is one way of addressing this challenge. In stream-based AL, observations are
continuously made available to the learner that have to decide whether to
request a label or to make a prediction. The goal is to reduce the request rate
while at the same time maximize prediction performance. In previous research,
reinforcement learning has been used for learning the AL request/prediction
strategy. In our work, we propose to equip a reinforcement learning process
with memory augmented neural networks, to enhance the one-shot capabilities.
Moreover, we introduce Class Margin Sampling (CMS) as an extension of the
standard margin sampling to the reinforcement learning setting. This strategy
aims to reduce training time and improve sample efficiency in the training
process. We evaluate the proposed method on a classification task using
empirical accuracy of label predictions and percentage of label requests. The
results indicates that the proposed method, by making use of the memory
augmented networks and CMS in the training process, outperforms existing
baselines.
