Refine to the essence: Less-redundant skill learning via diversity clustering-Reference-Cited by-同舟云学术

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Refine to the essence: Less-redundant skill learning via diversity clustering

Published:2024-07 Issue: Volume:133 Page:107981
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Qing Shuai^ORCID,Zhu Fei^ORCID

Funder

Priority Academic Program Development of Jiangsu Higher Education Institutions

National Natural Science Foundation of China

Jiangsu Province Natural Science Foundation

Publisher

Elsevier BV

Reference55 articles.

1. Marginal utility for planning in continuous or large discrete action spaces;Ahmad,2020

2. Skill discovery for exploration and planning using deep skill graphs;Bagaria,2021

3. Mutual information neural estimation;Belghazi,2018

4. OpenAI gym;Brockman,2016

5. Explore, discover and learn: Unsupervised discovery of state-covering skills;Campos,2020

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