  We address the problem of semi-supervised domain adaptation of classification
algorithms through deep Q-learning. The core idea is to consider the
predictions of a source domain network on target domain data as noisy labels,
and learn a policy to sample from this data so as to maximize classification
accuracy on a small annotated reward partition of the target domain. Our
experiments show that learned sampling policies construct labeled sets that
improve accuracies of visual classifiers over baselines.
