  We propose a hierarchical reinforcement learning method, HIDIO, that can
learn task-agnostic options in a self-supervised manner while jointly learning
to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL
approaches that tend to formulate goal-reaching low-level tasks or pre-define
ad hoc lower-level policies, HIDIO encourages lower-level option learning that
is independent of the task at hand, requiring few assumptions or little
knowledge about the task structure. These options are learned through an
intrinsic entropy minimization objective conditioned on the option
sub-trajectories. The learned options are diverse and task-agnostic. In
experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO
achieves higher success rates with greater sample efficiency than regular RL
baselines and two state-of-the-art hierarchical RL methods.
