# Griddly: A platform for AI research in games

arXiv: https://arxiv.org/abs/2011.06363 · HTML: https://mlsanity.com/paper/2011.06363/

## Key facts

- Cited by 31 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 30 of 31)

- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research](https://mlsanity.com/paper/2109.13202/) (2021)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [Level Generation Through Large Language Models](https://mlsanity.com/paper/2302.05817/) (2023)
- [XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX](https://mlsanity.com/paper/2312.12044/) (2023)
- [MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2303.03376/) (2023)
- [Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI](https://mlsanity.com/paper/2310.01824/) (2023)
- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning](https://mlsanity.com/paper/2311.03736/) (2023)
- [minimax: Efficient Baselines for Autocurricula in JAX](https://mlsanity.com/paper/2311.12716/) (2023)
- [Improving Intrinsic Exploration by Creating Stationary Objectives](https://mlsanity.com/paper/2310.18144/) (2023)
- [Generalising Discrete Action Spaces with Conditional Action Trees](https://mlsanity.com/paper/2104.07294/) (2021)
- [GriddlyJS: A Web IDE for Reinforcement Learning](https://mlsanity.com/paper/2207.06105/) (2022)
- [Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2405.17243/) (2024)
- [Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments](https://mlsanity.com/paper/2407.03969/) (2024)
- [CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/2111.08819/) (2021)
- [Did I do that? Blame as a means to identify controlled effects in reinforcement learning](https://mlsanity.com/paper/2106.00266/) (2021)
- [Watts: Infrastructure for Open-Ended Learning](https://mlsanity.com/paper/2204.13250/) (2022)
- [Syllabus: Portable Curricula for Reinforcement Learning Agents](https://mlsanity.com/paper/2411.11318/) (2024)
- [PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games](https://mlsanity.com/paper/2307.09905/) (2023)
- [Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games](https://mlsanity.com/paper/2312.02231/) (2023)
- [The Emergence of Complex Behavior in Large-Scale Ecological Environments](https://mlsanity.com/paper/2510.18221/) (2025)
- [Robust Agents in Open-Ended Worlds](https://mlsanity.com/paper/2512.08139/) (2025)
- [Predictive Control Using Learned State Space Models via Rolling Horizon Evolution](https://mlsanity.com/paper/2106.13911/) (2021)
- [A Research Agenda for Usability and Generalisation in Reinforcement Learning](https://mlsanity.com/paper/2412.16970/) (2024)
- [CoGrid & the Multi-User Gymnasium: A Framework for Multi-Agent Experimentation](https://mlsanity.com/paper/2604.15044/) (2026)

## Abstract (arXiv)

In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually implemented with their own preset environments and game mechanics, thus making it difficult for researchers to prototype different game environments. However, testing the RL agents against a variety of game environments is critical for recent effort to study generalization in RL and avoid the problem of overfitting that may otherwise occur. In this paper, we present Griddly as a new platform for Game AI research that provides a unique combination of highly configurable games, different observer types and an efficient C++ core engine. Additionally, we present a series of baseline experiments to study the effect of different observation configurations and generalization ability of RL agents.