  We connect a broad class of generative models through their shared reliance
on sequential decision making. Motivated by this view, we develop extensions to
an existing model, and then explore the idea further in the context of data
imputation -- perhaps the simplest setting in which to investigate the relation
between unconditional and conditional generative modelling. We formulate data
imputation as an MDP and develop models capable of representing effective
policies for it. We construct the models using neural networks and train them
using a form of guided policy search. Our models generate predictions through
an iterative process of feedback and refinement. We show that this approach can
learn effective policies for imputation problems of varying difficulty and
across multiple datasets.
