  Accurate and reliable prediction of hospital admission location is important
due to resource-constraints and space availability in a clinical setting,
particularly when dealing with patients who come from the emergency department.
In this work we propose a student-teacher network via reinforcement learning to
deal with this specific problem. A representation of the weights of the student
network is treated as the state and is fed as an input to the teacher network.
The teacher network's action is to select the most appropriate batch of data to
train the student network on from a training set sorted according to entropy.
By validating on three datasets, not only do we show that our approach
outperforms state-of-the-art methods on tabular data and performs competitively
on image recognition, but also that novel curricula are learned by the teacher
network. We demonstrate experimentally that the teacher network can actively
learn about the student network and guide it to achieve better performance than
if trained alone.
