keras-rl/keras-rl: papers linking to it

GitHub · 5,548 stars · 1,337 forks · Python · license MIT · created 2016 · github.com/keras-rl/keras-rl

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to keras-rl/keras-rl (https://github.com/keras-rl/keras-rl), 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#

6 papers in the tracked journals link to the GitHub repository keras-rl/keras-rl in their Crossref records, the first in 2020; most of them (2) in 2022.
By category: AI (6); the journals with the most are Applied Intelligence (3), Expert Systems with Applications (1) and Artificial Intelligence Review (1).

By year#

20202024

Papers linking to keras-rl/keras-rl by publication year
Year	Papers
2020	2
2021	1
2022	2
2024	1

Journals#

Journals whose papers link to keras-rl/keras-rl most
Journal	Papers
Applied Intelligence	3
Expert Systems with Applications	1
Artificial Intelligence Review	1
Machine Learning	1

Papers linking to keras-rl/keras-rl, most cited first
Published	Title	Journal	Authors	Citations	Link
2020-08-09	Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting	Expert Systems with Applications	Salvatore Carta, Anselmo Ferreira, Alessandro Sebastian Podda et al.	227	cites it
2020-09-07	A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning	Applied Intelligence	Salvatore Carta, Andrea Corriga, Anselmo Ferreira et al.	136	cites it
2022-07-02	A survey on deep reinforcement learning for audio-based applications	Artificial Intelligence Review	Siddique Latif, Heriberto Cuayáhuitl, Farrukh Pervez et al.	106	cites it
2021-07-07	Air Learning: a deep reinforcement learning gym for autonomous aerial robot visual navigation	Machine Learning	Srivatsan Krishnan, Behzad Boroujerdian, William Fu et al.	50	cites it
2024-06-26	LExCI: A framework for reinforcement learning with embedded systems	Applied Intelligence	Kevin Badalian, Lucas Koch, Tobias Brinkmann et al.	10	cites it
2022-05-19	Towards designing a generic and comprehensive deep reinforcement learning framework	Applied Intelligence	Ngoc Duy Nguyen, Thanh Thi Nguyen, Nhat Truong Pham et al.	7	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). keras-rl/keras-rl: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-keras-rl-keras-rl

@misc{scholar4ai-code-github-keras-rl-keras-rl,
  author = {{Scholar4AI}},
  title = {keras-rl/keras-rl: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-keras-rl-keras-rl}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).