  Hierarchical reinforcement learning deals with the problem of breaking down
large tasks into meaningful sub-tasks. Autonomous discovery of these sub-tasks
has remained a challenging problem. We propose a novel method of learning
sub-tasks by combining paradigms of routing in computer networks and graph
based skill discovery within the options framework to define meaningful
sub-goals. We apply the recent advancements of learning embeddings using
Riemannian optimisation in the hyperbolic space to embed the state set into the
hyperbolic space and create a model of the environment. In doing so we enforce
a global topology on the states and are able to exploit this topology to learn
meaningful sub-tasks. We demonstrate empirically, both in discrete and
continuous domains, how these embeddings can improve the learning of meaningful
sub-tasks.
