  In the context of MDPs with high-dimensional states, downstream tasks are
predominantly applied on a compressed, low-dimensional representation of the
original input space. A variety of learning objectives have therefore been used
to attain useful representations. However, these representations usually lack
interpretability of the different features. We present a novel approach that is
able to disentangle latent features into a controllable and an uncontrollable
partition. We illustrate that the resulting partitioned representations are
easily interpretable on three types of environments and show that, in a
distribution of procedurally generated maze environments, it is feasible to
interpretably employ a planning algorithm in the isolated controllable latent
partition.
