  Active inference is a first principles approach for understanding the brain
in particular, and sentient agents in general, with the single imperative of
minimizing free energy. As such, it provides a computational account for
modelling artificial intelligent agents, by defining the agent's generative
model and inferring the model parameters, actions and hidden state beliefs.
However, the exact specification of the generative model and the hidden state
space structure is left to the experimenter, whose design choices influence the
resulting behaviour of the agent. Recently, deep learning methods have been
proposed to learn a hidden state space structure purely from data, alleviating
the experimenter from this tedious design task, but resulting in an entangled,
non-interpreteable state space. In this paper, we hypothesize that such a
learnt, entangled state space does not necessarily yield the best model in
terms of free energy, and that enforcing different factors in the state space
can yield a lower model complexity. In particular, we consider the problem of
3D object representation, and focus on different instances of the ShapeNet
dataset. We propose a model that factorizes object shape, pose and category,
while still learning a representation for each factor using a deep neural
network. We show that models, with best disentanglement properties, perform
best when adopted by an active agent in reaching preferred observations.
