English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Disentangling Options with Hellinger Distance Regularizer