  Intrinsically motivated goal exploration processes enable agents to
autonomously sample goals to explore efficiently complex environments with
high-dimensional continuous actions. They have been applied successfully to
real world robots to discover repertoires of policies producing a wide
diversity of effects. Often these algorithms relied on engineered goal spaces
but it was recently shown that one can use deep representation learning
algorithms to learn an adequate goal space in simple environments. However, in
the case of more complex environments containing multiple objects or
distractors, an efficient exploration requires that the structure of the goal
space reflects the one of the environment. In this paper we show that using a
disentangled goal space leads to better exploration performances than an
entangled goal space. We further show that when the representation is
disentangled, one can leverage it by sampling goals that maximize learning
progress in a modular manner. Finally, we show that the measure of learning
progress, used to drive curiosity-driven exploration, can be used
simultaneously to discover abstract independently controllable features of the
environment.
