The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Griddly: A platform for AI research in games
Open accessAbstract
Griddly is an open-source project aimed to be a all-encompassing platform for grid-world based research. Griddly provides a highly optimized game state and rendering engine with a flexible high-level interface for configuring environments. Not only does Griddly offer simple interfaces for single, multi-player and RTS games, but also multiple methods of rendering, configurable partial observability and interfaces for procedural content generation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625
Temporal difference learning and TD-Gammon
Tesauro, G. (1995) · Communications of the ACM · doi:10.1145/203330.203343 · Cited by 1,618
Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, :, Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.06680 · Cited by 1,065
Exploration by Random Network Distillation
Burda, Y., Edwards, H., Storkey, A., & Klimov, O. (2018) · arXiv · doi:10.48550/arxiv.1810.12894 · Cited by 527
Research works printed but not linked: 3
- Vinyals, 2019, Grandmaster level in StarCraft II using multi-agent reinforcement learning, Nature, 575, 350, 10.1038/s41586-019-1724-z · DOI · Paper page
- Perez, 2019, General video game AI: a multi-track framework for evaluating agents, games and content generation algorithms, IEEE Trans. Games, 1
- S. Ontanón, The combinatorial multi-armed bandit problem and its application to real-time strategy games, in: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2013, vol. 9, no. 1. · DOI
Cited by
Sorted by citations
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv · doi:10.48550/arxiv.2303.03376 · Cited by 8
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Nikulin, A., Kurenkov, V., Zisman, I., Agarkov, A., Sinii, V., & Kolesnikov, S. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-1390 · Cited by 3
“Other libraries, such as the popular MiniGrid (Chevalier-Boisvert et al., 2023) and Griddly (Bamford et al., 2020), are not scalable and extensible enough to cover meta-learning needs.”
BackgroundNeural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning
Suarez, J., Bloomin, D., Choe, K. W., Li, H. X., Sullivan, R., Kanna, N., Scott, D., Shuman, R., Bradley, H., Castricato, L., et al. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2178 · Cited by 1
Metadata
- Authors
- Bamford, C.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Bamford, C.
- Venue
- Software Impacts
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 8
- Pages
- 100066
- ISSN
- 2665-9638 (ISSN-L)
- OpenAlex
- W3103223352
- Cited by
- 17
- References
- 4
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 4
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Software Impacts (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Queen Mary University of London
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- multiplayer games, real-time strategy games, partial observability, procedural content generation, open source software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Griddly: A platform for AI research in games. https://app.cochat.ai/papers/10.1016/j.simpa.2021.100066
- The paper
- Bamford, C. (2021). Griddly: A platform for AI research in games. Software Impacts. https://doi.org/10.1016/j.simpa.2021.100066
