  Sparse-reward domains are challenging for reinforcement learning algorithms
since significant exploration is needed before encountering reward for the
first time. Hierarchical reinforcement learning can facilitate exploration by
reducing the number of decisions necessary before obtaining a reward. In this
paper, we present a novel hierarchical reinforcement learning framework based
on the compression of an invariant state space that is common to a range of
tasks. The algorithm introduces subtasks which consist of moving between the
state partitions induced by the compression. Results indicate that the
algorithm can successfully solve complex sparse-reward domains, and transfer
knowledge to solve new, previously unseen tasks more quickly.
