  A structured understanding of our world in terms of objects, relations, and
hierarchies is an important component of human cognition. Learning such a
structured world model from raw sensory data remains a challenge. As a step
towards this goal, we introduce Contrastively-trained Structured World Models
(C-SWMs). C-SWMs utilize a contrastive approach for representation learning in
environments with compositional structure. We structure each state embedding as
a set of object representations and their relations, modeled by a graph neural
network. This allows objects to be discovered from raw pixel observations
without direct supervision as part of the learning process. We evaluate C-SWMs
on compositional environments involving multiple interacting objects that can
be manipulated independently by an agent, simple Atari games, and a
multi-object physics simulation. Our experiments demonstrate that C-SWMs can
overcome limitations of models based on pixel reconstruction and outperform
typical representatives of this model class in highly structured environments,
while learning interpretable object-based representations.
