# Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games

arXiv: https://arxiv.org/abs/2312.02231 · HTML: https://mlsanity.com/paper/2312.02231/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design](https://mlsanity.com/paper/2012.02096/) (2020)
- [Prioritized Level Replay](https://mlsanity.com/paper/2010.03934/) (2020)
- [Evolving Curricula with Regret-Based Environment Design](https://mlsanity.com/paper/2203.01302/) (2022)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions](https://mlsanity.com/paper/2003.08536/) (2020)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [OMNI: Open-endedness via Models of human Notions of Interestingness](https://mlsanity.com/paper/2306.01711/) (2023)
- [Growing 3D Artefacts and Functional Machines with Neural Cellular Automata](https://mlsanity.com/paper/2103.08737/) (2021)
- [Illuminating Diverse Neural Cellular Automata for Level Generation](https://mlsanity.com/paper/2109.05489/) (2021)
- [Griddly: A platform for AI research in games](https://mlsanity.com/paper/2011.06363/) (2020)
- [EvoCraft: A New Challenge for Open-Endedness](https://mlsanity.com/paper/2012.04751/) (2020)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels](https://mlsanity.com/paper/2204.06934/) (2022)
- [Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales](https://mlsanity.com/paper/2002.03896/) (2020)
- [Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI](https://mlsanity.com/paper/2008.11258/) (2020)
- [GriddlyJS: A Web IDE for Reinforcement Learning](https://mlsanity.com/paper/2207.06105/) (2022)
- [Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs](https://mlsanity.com/paper/2306.13169/) (2023)
- [Exploring open-ended gameplay features with Micro RollerCoaster Tycoon](https://mlsanity.com/paper/2105.04342/) (2021)
- [Open-ended search for environments and adapted agents using MAP-Elites](https://mlsanity.com/paper/2305.01153/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)

## Abstract (arXiv)

We explore the generation of diverse environments using the Amorphous Fortress (AF) simulation framework. AF defines a set of Finite State Machine (FSM) nodes and edges that can be recombined to control the behavior of agents in the `fortress' grid-world. The behaviors and conditions of the agents within the framework are designed to capture the common building blocks of multi-agent artificial life and reinforcement learning environments. Using quality diversity evolutionary search, we generate diverse sets of environments. These environments exhibit certain types of complexity according to measures of agents' FSM architectures and activations, and collective behaviors. Our approach, Quality Diversity in Amorphous Fortress (QD-AF) generates families of 0-player games akin to simplistic ecological models, and we identify the emergence of both competitive and co-operative multi-agent and multi-species survival dynamics. We argue that these generated worlds can collectively serve as training and testing grounds for learning algorithms.