Computational Theories of Curiosity-Driven Learning: papers linking to it

OSF · Text · 2018 · osf.io/3p8f6/

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Computational Theories of Curiosity-Driven Learning (https://osf.io/3p8f6/), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the OSF dataset Computational Theories of Curiosity-Driven Learning in their Crossref records, the first in 2022; most of them (2) in 2022.
By category: AI (3); the journals with the most are Nature Machine Intelligence (2) and Cognitive Computation (1).

By year#

20222023

Papers linking to Computational Theories of Curiosity-Driven Learning by publication year
Year	Papers
2022	2
2023	1

Journals#

Journals whose papers link to Computational Theories of Curiosity-Driven Learning most
Journal	Papers
Nature Machine Intelligence	2
Cognitive Computation	1

Papers linking to Computational Theories of Curiosity-Driven Learning, most cited first
Published	Title	Journal	Authors	Citations	Link
2022-01-25	Intelligent problem-solving as integrated hierarchical reinforcement learning	Nature Machine Intelligence	Manfred Eppe, Christian Gumbsch, Matthias Kerzel et al.	99	cites it
2022-06-22	Lessons from infant learning for unsupervised machine learning	Nature Machine Intelligence	Lorijn Zaadnoordijk, Tarek R. Besold, Rhodri Cusack	66	cites it
2023-10-28	Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning	Cognitive Computation	Pankayaraj Pathmanathan, Natalia Díaz-Rodríguez, Javier Del Ser	1	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Computational Theories of Curiosity-Driven Learning: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/osf-3p8f6

@misc{scholar4ai-code-osf-3p8f6,
  author = {{Scholar4AI}},
  title = {Computational Theories of Curiosity-Driven Learning: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/osf-3p8f6}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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