  Autonomous agents need large repertoires of skills to act reasonably on new
tasks that they have not seen before. However, acquiring these skills using
only a stream of high-dimensional, unstructured, and unlabeled observations is
a tricky challenge for any autonomous agent. Previous methods have used
variational autoencoders to encode a scene into a low-dimensional vector that
can be used as a goal for an agent to discover new skills. Nevertheless, in
compositional/multi-object environments it is difficult to disentangle all the
factors of variation into such a fixed-length representation of the whole
scene. We propose to use object-centric representations as a modular and
structured observation space, which is learned with a compositional generative
world model. We show that the structure in the representations in combination
with goal-conditioned attention policies helps the autonomous agent to discover
and learn useful skills. These skills can be further combined to address
compositional tasks like the manipulation of several different objects.
