mlsanityData to 7 Oct 2026

Craftium: Bridging Flexibility and Efficiency for Rich 3D Single- and Multi-Agent Environments

arXiv 2407.03969 ↗First posted 4 Jul 2024cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Advances in large models, reinforcement learning, and open-endedness have accelerated progress toward autonomous agents that can learn and interact in the real world. To achieve this, flexible tools are needed to create rich, yet computationally efficient, environments. While scalable 2D environments fail to address key real-world challenges like 3D navigation and spatial reasoning, more complex 3D environments are computationally expensive and lack features like customizability and multi-agent support. This paper introduces Craftium, a highly customizable and easy-to-use platform for building rich 3D single- and multi-agent environments. We showcase environments of different complexity and nature: from single- and multi-agent tasks to vast worlds with many creatures and biomes, and customizable procedural task generators. Benchmarking shows that Craftium significantly reduces the computational cost of alternatives of similar richness, achieving +2K steps per second more than Minecraft-based frameworks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 49 of its 72 references that are in the map. First posted on arXiv 4 Jul 2024.

Builds on

49 of 72 references.

References · 49

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  5. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  6. 2018Exploration by Random Network Distillationcited by 966
  7. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 850
  8. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 679
  9. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  10. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  11. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 478
  12. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
37 more
  1. 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 369
  2. 2023Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Taskscited by 320
  3. 2023ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skillscited by 313
  4. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
  5. 2017Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agentscited by 267
  6. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 260
  7. 2017Count-Based Exploration with Neural Density Modelscited by 255
  8. 2020ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulationcited by 249
  9. 2023Habitat 3.0: A Co-Habitat for Humans, Avatars and Robotscited by 220
  10. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 210
  11. 2020Never Give Up: Learning Directed Exploration Strategiescited by 210
  12. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  13. 2023RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulationcited by 206
  14. 2020The NetHack Learning Environmentcited by 196
  15. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  16. 2021Continual World: A Robotic Benchmark For Continual Reinforcement Learningcited by 112
  17. 2023A Definition of Continual Reinforcement Learningcited by 98
  18. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  19. 2021MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Researchcited by 96
  20. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 80
  21. 2024Open-Endedness is Essential for Artificial Superhuman Intelligencecited by 79
  22. 2024Scaling Instructable Agents Across Many Simulated Worldscited by 67
  23. 2024OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Codecited by 67
  24. 2018ViZDoom Competitions: Playing Doom from Pixelscited by 50
  25. 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 35
  26. 2020Griddly: A platform for AI research in gamescited by 31
  27. 2022Building a Subspace of Policies for Scalable Continual Learningcited by 28
  28. 2020EvoCraft: A New Challenge for Open-Endednesscited by 23
  29. 2024Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problemcited by 23
  30. 2024DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraftcited by 17
  31. 2024Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalizationcited by 16
  32. 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilitiescited by 15
  33. 2024Position: Benchmarking is Limited in Reinforcement Learning Researchcited by 15
  34. 2023Reward-Free Curricula for Training Robust World Modelscited by 13
  35. 2024MAMBA: an Effective World Model Approach for Meta-Reinforcement Learningcited by 13
  36. 2024DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Designcited by 11
  37. 2023Learning Diverse Risk Preferences in Population-based Self-playcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026MineExplorer: Evaluating Open-World Exploration of MLLM Agents in Minecraft
  2. 2025CrafterDojo: A Suite of Foundation Models for Building Open-Ended Embodied Agents in Crafter
  3. 2024TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft
  4. 2024MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs
  5. 2024Odyssey: Empowering Minecraft Agents with Open-World Skills
  6. 2025Training Agents Inside of Scalable World Models
  7. 2025Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents
  8. 2025OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft