# EvoCraft: A New Challenge for Open-Endedness

arXiv: https://arxiv.org/abs/2012.04751 · HTML: https://mlsanity.com/paper/2012.04751/

## Key facts

- Cited by 23 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress](https://mlsanity.com/paper/2206.01626/) (2022)
- [Growing 3D Artefacts and Functional Machines with Neural Cellular Automata](https://mlsanity.com/paper/2103.08737/) (2021)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [General Intelligence Requires Rethinking Exploration](https://mlsanity.com/paper/2211.07819/) (2022)
- [Learning Controllable 3D Level Generators](https://mlsanity.com/paper/2206.13623/) (2022)
- [DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraft](https://mlsanity.com/paper/2404.15538/) (2024)
- [Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition](https://mlsanity.com/paper/2303.13512/) (2023)
- [World-GAN: a Generative Model for Minecraft Worlds](https://mlsanity.com/paper/2106.10155/) (2021)
- [Evolution and The Knightian Blindspot of Machine Learning](https://mlsanity.com/paper/2501.13075/) (2025)
- [BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks](https://mlsanity.com/paper/2312.02405/) (2023)
- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)
- [Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning](https://mlsanity.com/paper/2109.03554/) (2021)
- [The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games](https://mlsanity.com/paper/2503.21474/) (2025)
- [Evolving Flying Machines in Minecraft Using Quality Diversity](https://mlsanity.com/paper/2302.00782/) (2023)
- [Hierarchically Composing Level Generators for the Creation of Complex Structures](https://mlsanity.com/paper/2302.01561/) (2023)
- [Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments](https://mlsanity.com/paper/2407.03969/) (2024)
- [Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity](https://mlsanity.com/paper/2107.05786/) (2021)
- [A Compositional Framework for Open-ended Intelligence](https://mlsanity.com/paper/2606.15386/) (2026)
- [Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games](https://mlsanity.com/paper/2312.02231/) (2023)
- [Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes](https://mlsanity.com/paper/2604.22847/) (2026)

## Abstract (arXiv)

This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicating with Minecraft to place and track blocks. In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion. Compared to other environments used to study open-endedness, Minecraft allows the construction of almost any kind of structure, including actuated machines with circuits and mechanical components. We present initial baseline results in evolving simple Minecraft creations through both interactive and automated evolution. While evolution succeeds when tasked to grow a structure towards a specific target, it is unable to find a solution when rewarded for creating a simple machine that moves. Thus, EvoCraft offers a challenging new environment for automated search methods (such as evolution) to find complex artifacts that we hope will spur the development of more open-ended algorithms. A Python implementation of the EvoCraft framework is available at: https://github.com/real-itu/Evocraft-py.