---
title: "openai/baselines: papers citing this GitHub repository"
description: "10 papers in the tracked journals link to the GitHub repository openai/baselines in their Crossref records, the first in 2019; most of them (3) in 2022."
url: https://scholar4ai.com/code/github-openai-baselines
markdown_url: https://scholar4ai.com/code/github-openai-baselines.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "10 papers in the tracked journals link to the GitHub repository openai/baselines in their Crossref records, the first in 2019; most of them (3) in 2022. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# openai/baselines: papers linking to it

Research papers in the 480 tracked journals that link to openai/baselines (https://github.com/openai/baselines), 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.

- GitHub
- 16,775 stars
- 4,918 forks
- Python
- license MIT
- created 2017
- https://github.com/openai/baselines

All repositories: https://scholar4ai.com/code.md

## Key findings

- 10 papers in the tracked journals link to the GitHub repository openai/baselines in their Crossref records, the first in 2019; most of them (3) in 2022.
- By category: AI (10); the journals with the most are Applied Intelligence (3), Expert Systems with Applications (2) and Machine Learning (2).
- Linked repositories derived from it: 1, such as hill-a/stable-baselines.

## By year

*Papers linking to openai/baselines by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 2 |
| 2021 | 2 |
| 2022 | 3 |
| 2023 | 1 |
| 2025 | 1 |

## Journals

*Journals whose papers link to openai/baselines most*

| Journal | Papers |
|---|---:|
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 3 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2 |
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 2 |
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |

## Related repositories

*Linked repositories openai/baselines derives from or that derive from it*

| Repository | Relation | Papers |
|---|---|---:|
| [hill-a/stable-baselines](https://scholar4ai.com/code/github-hill-a-stable-baselines.md) | fork of openai/baselines | 12 |

## Papers

*Papers linking to openai/baselines, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-10-29 | [Reward-driven U-Net training for obstacle avoidance drone](https://doi.org/10.1016/j.eswa.2019.113064) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Sangyun Shin, Yong-Won Kang, Yong-Guk Kim | 42 | cites it |
| 2022-03-14 | [Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks](https://doi.org/10.1038/s42256-022-00446-y) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Yuqin Chen, Yu Chen, Chee‐Kong Lee et al. | 38 | cites it |
| 2020-09-16 | [Fast and slow curiosity for high-level exploration in reinforcement learning](https://doi.org/10.1007/s10489-020-01849-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Nicolas Bougie, Ryutaro Ichise | 32 | cites it |
| 2020-06-01 | [Combining a gradient-based method and an evolution strategy for multi-objective reinforcement learning](https://doi.org/10.1007/s10489-020-01702-7) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Diqi Chen, Yizhou Wang, Wen Gao | 31 | cites it |
| 2021-05-07 | [Importance sampling in reinforcement learning with an estimated behavior policy](https://doi.org/10.1007/s10994-020-05938-9) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Josiah P. Hanna, Scott Niekum, Peter Stone | 27 | cites it |
| 2022-03-23 | [MoËT: Mixture of Expert Trees and its application to verifiable reinforcement learning](https://doi.org/10.1016/j.neunet.2022.03.022) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Marko Vasić, Andrija Petrović, Kaiyuan Wang et al. | 26 | cites it |
| 2022-05-19 | [Towards designing a generic and comprehensive deep reinforcement learning framework](https://doi.org/10.1007/s10489-022-03550-z) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Ngoc Duy Nguyen, Thanh Thi Nguyen, Nhat Truong Pham et al. | 7 | cites it |
| 2023-10-26 | [Deep intrinsically motivated exploration in continuous control](https://doi.org/10.1007/s10994-023-06363-4) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Baturay Sağlam, Süleyman S. Kozat | 5 | cites it |
| 2021-03-03 | [Self-guided deep deterministic policy gradient with multi-actor](https://doi.org/10.1007/s00521-021-05738-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Hongming Chen, Quan Liu, Shan Zhong | 4 | cites it |
| 2025-12-04 | [CLARIFID: Improving radiology report generation by reinforcing clinically accurate impressions and enforcing detailed findings](https://doi.org/10.1016/j.eswa.2025.130633) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Kyeongkyu Lee, Seonghwan Yoon, Hongki Lim | 2 | 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). openai/baselines: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-openai-baselines

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

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).