  Path of Destruction (PoD) is a self-supervised method for learning iterative
generators. The core idea is to produce a training set by destroying a set of
artifacts, and for each destructive step create a training instance based on
the corresponding repair action. A generator trained on this dataset can then
generate new artifacts by repairing from arbitrary states. The PoD method is
very data-efficient in terms of original training examples and well-suited to
functional artifacts composed of categorical data, such as game levels and
discrete 3D structures. In this paper, we extend the Path of Destruction method
to allow designer control over aspects of the generated artifacts.
Controllability is introduced by adding conditional inputs to the state-action
pairs that make up the repair trajectories. We test the controllable PoD method
in a 2D dungeon setting, as well as in the domain of small 3D Lego cars.
