mlsanityData to 7 Oct 2026

EvoCraft: A New Challenge for Open-Endedness

arXiv 2012.04751 ↗First posted 8 Dec 2020cs.AIcited by 23

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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.

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

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Citing papers per year: 2021: 5; 2022: 4; 2023: 7; 2024: 3; 2025: 2; 2026: 2.

23 papers.

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  1. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  2. 2021Open-Ended Learning Leads to Generally Capable Agentscited by 177
  3. 2023Human-Timescale Adaptation in an Open-Ended Task Spacecited by 136
  4. 2022Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progresscited by 63
  5. 2021Growing 3D Artefacts and Functional Machines with Neural Cellular Automatacited by 38
  6. 2023Creative Agents: Empowering Agents with Imagination for Creative Taskscited by 24
  7. 2022General Intelligence Requires Rethinking Explorationcited by 20
  8. 2022Learning Controllable 3D Level Generatorscited by 19
  9. 2024DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraftcited by 17
  10. 2023Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competitioncited by 15
  11. 2021World-GAN: a Generative Model for Minecraft Worldscited by 14
  12. 2025Evolution and The Knightian Blindspot of Machine Learningcited by 11
11 more
  1. 2023BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Taskscited by 8
  2. 2024From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Modelscited by 8
  3. 2021Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learningcited by 5
  4. 2025The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Gamescited by 5
  5. 2023Evolving Flying Machines in Minecraft Using Quality Diversitycited by 4
  6. 2023Hierarchically Composing Level Generators for the Creation of Complex Structurescited by 4
  7. 2024Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environmentscited by 4
  8. 2021Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativitycited by 3
  9. 2026A Compositional Framework for Open-ended Intelligencecited by 2
  10. 2023Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Gamescited by 1
  11. 2026Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubescited by 0