  This paper presents a framework for efficiently learning feature selection
policies which use less features to reach a high classification precision on
large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for
learning compact feature spaces, in combination with recently-proposed
Reinforcement Learning (RL) algorithms as Double DQN and Retrace.
