# Disentangling Options with Hellinger Distance Regularizer

## TL;DR

This paper introduces a Hellinger distance regularizer to improve the disentanglement of options in reinforcement learning, addressing the mutual exclusivity of learned options and providing statistical indicators for comparison.

## Contribution

The paper proposes a novel Hellinger distance regularizer for disentangling options in RL and offers statistical tools to evaluate learned options against existing methods.

## Key findings

- The regularizer effectively disentangles options in RL.
- Statistical indicators provide better comparison of options.
- Improved mutual exclusivity among options.

## Abstract

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.