mlsanityData to 7 Oct 2026

MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned

arXiv 2202.10583 ↗First posted 17 Feb 2022cs.LG cs.AIcited by 26

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Abstract (arXiv)

Reinforcement learning competitions advance the field by providing appropriate scope and support to develop solutions toward a specific problem. To promote the development of more broadly applicable methods, organizers need to enforce the use of general techniques, the use of sample-efficient methods, and the reproducibility of the results. While beneficial for the research community, these restrictions come at a cost -- increased difficulty. If the barrier for entry is too high, many potential participants are demoralized. With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers. With this track and more extensive tutorials and support, we saw an increased number of submissions. The participants of this easier track were able to obtain a diamond, and the participants of the harder track progressed the generalizable solutions in the same task.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

30 of 43 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2017Proximal Policy Optimization Algorithmscited by 14,441
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
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  7. 2017Deep Reinforcement Learning that Matterscited by 727
  8. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 478
  9. 2019MineRL: A Large-Scale Dataset of Minecraft Demonstrationscited by 210
  10. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
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18 more
  1. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  2. 2021Pretrained Transformers as Universal Computation Enginescited by 162
  3. 2020Keep CALM and Explore: Language Models for Action Generation in Text-based Gamescited by 108
  4. 2022Can Wikipedia Help Offline Reinforcement Learning?cited by 76
  5. 2019Obstacle Tower: A Generalization Challenge in Vision, Control, and Planningcited by 54
  6. 2018ViZDoom Competitions: Playing Doom from Pixelscited by 50
  7. 2020Flatland-RL : Multi-Agent Reinforcement Learning on Trainscited by 32
  8. 2021JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learningcited by 28
  9. 2021Language Models are Few-Shot Butlerscited by 27
  10. 2021The MineRL BASALT Competition on Learning from Human Feedbackcited by 24
  11. 2021Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmarkcited by 23
  12. 2021SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competitioncited by 20
  13. 2020Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learningcited by 18
  14. 2021NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environmentcited by 15
  15. 2019The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) Competitioncited by 14
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  17. 2020Guaranteeing Reproducibility in Deep Learning Competitionscited by 4
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Citing papers per year: 2022: 3; 2023: 11; 2024: 7; 2025: 5.

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  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  2. 2022MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledgecited by 469
  3. 2023Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agentscited by 396
  4. 2022Transformers are Sample-Efficient World Modelscited by 317
  5. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
  6. 2023JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Modelscited by 170
  7. 2023Human-Timescale Adaptation in an Open-Ended Task Spacecited by 136
  8. 2023Language Models Meet World Models: Embodied Experiences Enhance Language Modelscited by 123
  9. 2025Training Agents Inside of Scalable World Modelscited by 122
  10. 2023Towards A Unified Agent with Foundation Modelscited by 46
  11. 2025Latent Action Learning Requires Supervision in the Presence of Distractorscited by 40
  12. 2023Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Predictioncited by 39
14 more
  1. 2024Efficient World Models with Context-Aware Tokenizationcited by 33
  2. 2024RL-GPT: Integrating Reinforcement Learning and Code-as-policycited by 32
  3. 2024S-Agents: Self-organizing Agents in Open-ended Environmentscited by 22
  4. 2025SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactionscited by 16
  5. 2022IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022cited by 14
  6. 2023Reinforcement Learning Friendly Vision-Language Model for Minecraftcited by 14
  7. 2024ADAM: An Embodied Causal Agent in Open-World Environmentscited by 13
  8. 2025AssistanceZero: Scalably Solving Assistance Gamescited by 10
  9. 2024Affordable Generative Agentscited by 8
  10. 2023Behavioral Cloning via Search in Embedded Demonstration Datasetcited by 3
  11. 2023Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real Worldcited by 3
  12. 2024Online Adaptation for Enhancing Imitation Learning Policiescited by 3
  13. 2024Massively Multiagent Minigames for Training Generalist Agentscited by 0
  14. 2025A Measure Based Generalizable Approach to Understandabilitycited by 0