  In this paper, we will evaluate the performance of graph neural networks in
two distinct domains: computer vision and reinforcement learning. In the
computer vision section, we seek to learn whether a novel non-redundant
representation for images as graphs can improve performance over trivial pixel
to node mapping on a graph-level prediction graph, specifically image
classification. For the reinforcement learning section, we seek to learn if
explicitly modeling solving a Rubik's cube as a graph problem can improve
performance over a standard model-free technique with no inductive bias.
