---
title: "Farama-Foundation/Minigrid: papers citing it"
description: "6 papers in the tracked journals link to the GitHub repository Farama-Foundation/Minigrid in their Crossref records, the first in 2019; most of them (2) in 2025."
url: https://scholar4ai.com/code/github-farama-foundation-minigrid
markdown_url: https://scholar4ai.com/code/github-farama-foundation-minigrid.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to the GitHub repository Farama-Foundation/Minigrid in their Crossref records, the first in 2019; most of them (2) in 2025. (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)
---

# Farama-Foundation/Minigrid: papers linking to it

Research papers in the 480 tracked journals that link to Farama-Foundation/Minigrid (https://github.com/Farama-Foundation/Minigrid), 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
- 2,535 stars
- 668 forks
- Python
- created 2017
- https://github.com/Farama-Foundation/Minigrid

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

## Key findings

- 6 papers in the tracked journals link to the GitHub repository Farama-Foundation/Minigrid in their Crossref records, the first in 2019; most of them (2) in 2025.
- By category: AI (6); the journals with the most are Applied Intelligence (2), Neural Computing and Applications (2) and Neural Networks (1).
- 3 of the papers build on one another: a later paper cites an earlier one that also links Farama-Foundation/Minigrid.

## By year

*Papers linking to Farama-Foundation/Minigrid by publication year*

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

## Journals

*Journals whose papers link to Farama-Foundation/Minigrid most*

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

## Family tree

Each paper sits under the earlier paper it builds on.

- [Skill-based curiosity for intrinsically motivated reinforcement learning](https://doi.org/10.1007/s10994-019-05845-8) (Machine Learning, 2019)
  - [Fast and slow curiosity for high-level exploration in reinforcement learning](https://doi.org/10.1007/s10489-020-01849-3) (Applied Intelligence, 2020) — cites the paper above
    - [Hierarchical learning from human preferences and curiosity](https://doi.org/10.1007/s10489-021-02726-3) (Applied Intelligence, 2021) — cites the paper above

## Papers

*Papers linking to Farama-Foundation/Minigrid, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 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 |
| 2019-10-10 | [Skill-based curiosity for intrinsically motivated reinforcement learning](https://doi.org/10.1007/s10994-019-05845-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Nicolas Bougie, Ryutaro Ichise | 31 | cites it |
| 2021-09-28 | [Hierarchical learning from human preferences and curiosity](https://doi.org/10.1007/s10489-021-02726-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Nicolas Bougie, Ryutaro Ichise | 10 | cites it |
| 2022-09-04 | [Influence-aware memory architectures for deep reinforcement learning in POMDPs](https://doi.org/10.1007/s00521-022-07691-7) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Miguel Suau, Jinke He, Elena Congeduti et al. | 5 | cites it |
| 2025-07-16 | [Representation-driven sampling and adaptive policy resetting for improving multi-Agent reinforcement learning](https://doi.org/10.1016/j.neunet.2025.107875) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Weiqiang Jin, Xingwu Tian, Ningwei Wang et al. | 3 | cites it |
| 2025-04-12 | [Experiential Explanations for Reinforcement Learning](https://doi.org/10.1007/s00521-024-10951-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Amal Alabdulkarim, Madhuri Singh, Gennie Mansi et al. | 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). Farama-Foundation/Minigrid: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-farama-foundation-minigrid

```bibtex
@misc{scholar4ai-code-github-farama-foundation-minigrid,
  author = {{Scholar4AI}},
  title = {Farama-Foundation/Minigrid: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-farama-foundation-minigrid}},
  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/).