# The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

arXiv: https://arxiv.org/abs/1904.10079 · HTML: https://mlsanity.com/paper/1904.10079/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2019.

## Built on by (most cited first, 26 of 26)

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- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction](https://mlsanity.com/paper/2301.10034/) (2023)
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- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [Towards Solving Fuzzy Tasks with Human Feedback: A Retrospective of the MineRL BASALT 2022 Competition](https://mlsanity.com/paper/2303.13512/) (2023)
- [Reinforcement Learning Friendly Vision-Language Model for Minecraft](https://mlsanity.com/paper/2303.10571/) (2023)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
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- [BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks](https://mlsanity.com/paper/2312.02405/) (2023)
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- [Approximate Model-Based Shielding for Safe Reinforcement Learning](https://mlsanity.com/paper/2308.00707/) (2023)
- [MilliVid: Hierarchical Latents for Long-Range Consistency in Video Generation](https://mlsanity.com/paper/2606.09056/) (2026)
- [Distilling Reinforcement Learning Tricks for Video Games](https://mlsanity.com/paper/2107.00703/) (2021)
- [AI Finds A Way](https://mlsanity.com/paper/2608.23875/) (2026)
- [AI for Games in the Foundation Model Era](https://mlsanity.com/paper/2609.16679/) (2026)
- [Approximate Shielding of Atari Agents for Safe Exploration](https://mlsanity.com/paper/2304.11104/) (2023)
- [Contrastive Abstraction for Reinforcement Learning](https://mlsanity.com/paper/2410.00704/) (2024)
- [Believable Minecraft Settlements by Means of Decentralised Iterative Planning](https://mlsanity.com/paper/2309.10871/) (2023)
- [Results of the NeurIPS 2023 Neural MMO Competition on Multi-task Reinforcement Learning](https://mlsanity.com/paper/2508.12524/) (2025)

## Abstract (arXiv)

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.