  We present HiDe, a novel hierarchical reinforcement learning architecture
that successfully solves long horizon control tasks and generalizes to unseen
test scenarios. Functional decomposition between planning and low-level control
is achieved by explicitly separating the state-action spaces across the
hierarchy, which allows the integration of task-relevant knowledge per layer.
We propose an RL-based planner to efficiently leverage the information in the
planning layer of the hierarchy, while the control layer learns a
goal-conditioned control policy. The hierarchy is trained jointly but allows
for the modular transfer of policy layers across hierarchies of different
agents. We experimentally show that our method generalizes across unseen test
environments and can scale to 3x horizon length compared to both learning and
non-learning based methods. We evaluate on complex continuous control tasks
with sparse rewards, including navigation and robot manipulation.
