  Hierarchy in reinforcement learning agents allows for control at multiple
time scales yielding improved sample efficiency, the ability to deal with long
time horizons and transferability of sub-policies to tasks outside the training
distribution. It is often implemented as a master policy providing goals to a
sub-policy. Ideally, we would like the goal-spaces to be learned, however,
properties of optimal goal spaces still remain unknown and consequently there
is no method yet to learn optimal goal spaces. Motivated by this, we
systematically analyze how various modifications to the ground-truth goal-space
affect learning in hierarchical models with the aim of identifying important
properties of optimal goal spaces. Our results show that, while rotation of
ground-truth goal spaces and noise had no effect, having additional unnecessary
factors significantly impaired learning in hierarchical models.
