  In reinforcement learning (RL), temporal abstraction still remains as an
important and unsolved problem. The options framework provided clues to
temporal abstraction in the RL, and the option-critic architecture elegantly
solved the two problems of finding options and learning RL agents in an
end-to-end manner. However, it is necessary to examine whether the options
learned through this method play a mutually exclusive role. In this paper, we
propose a Hellinger distance regularizer, a method for disentangling options.
In addition, we will shed light on various indicators from the statistical
point of view to compare with the options learned through the existing
option-critic architecture.
