Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoretical and intuitive motivations. In contrast to this body of research, we propose to treat active learning algorithm design as a meta-learning problem and learn the best criterion from data. We model an active learning algorithm as a deep neural network that inputs the base learner state and the unlabelled point set and predicts the best point to annotate next. Training this active query policy network with reinforcement learning, produces the best non-myopic policy for a given dataset. The key challenge in achieving a general solution to AL then becomes that of learner generalisation, particularly across heterogeneous datasets. We propose a multi-task dataset-embedding approach that allows dataset-agnostic active learners to be trained. Our evaluation shows that AL algorithms trained in this way can directly generalise across diverse problems.
The approach solves an important problem as getting labelled data is hard. The focus is on the key aspect, which is generalisation across heteregeneous data. The novel idea is the dataset embedding so that their RL policy can be trained to work across diverse datasets.
Pros: 
1. The approach performs well against all the baselines, and also achieves good cross-task generalisation in the tasks they evaluated on. 
2. In particular, they alsoevaluated on test datasets with fairly different statistics from the training datasets, which isnt very common in most meta-learning papers today, so it’s encouraging that the method works in that regime.
Cons: 
1. The embedding strategy, especially the representative and discriminative histograms, is complicated. It is unclear if the strategy is general enough to work on harder problems / larger datasets, or with higher dimensional data like images. More evidence in the paper for why it would work on harder problems would be great. 
2. The policy network would have to output a probability for each datapoint in the dataset U, which could be fairly large, thus the method is computationally much more expensive than random sampling. A section devoted to showing what practical problems could be potentially solved by this method would be useful.
3. It is unclear to me if the results in table 3 and 4 are achieved by retraining from scratch with an RBF SVM, or by freezing the policy network trained on a linear SVM and directly evaluating it with a RBF SVM base learner.
Significance/Conclusion: The idea of meta-learning or learning to learn is fairly common now. While they do show good performance, it’s unclear if the specific embedding strategy suggested in this paper will generalise to harder tasks. 
Comments: There’s lots of typos, please proof read to improve the paper.
Revision: I thank the authors for the updates and addressing some of my concerns. I agree the computational budget makes sense for cross data transfer, however the embedding strategy and lack of larger experiments makes it unclear if it'll generalise to harder tasks. I update my review to 6.