  Training a reinforcement learning agent to carry out natural language
instructions is limited by the available supervision, i.e. knowing when the
instruction has been carried out. We adapt the CLEVR visual question answering
dataset to generate complex natural language navigation instructions and
accompanying scene graphs, yielding an environment-agnostic supervised dataset.
To demonstrate the use of this data set, we map the scenes to the VizDoom
environment and use the architecture in \citet{gatedattention} to train an
agent to carry out these more complex language instructions.

Machine Learning;Computation and Language;Computer Vision and Pattern Recognition