# The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

arXiv: https://arxiv.org/abs/2101.11071 · HTML: https://mlsanity.com/paper/2101.11071/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [A Deep Hierarchical Approach to Lifelong Learning in Minecraft](https://mlsanity.com/paper/1604.07255/) (2016)
- [Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1712.07294/) (2017)
- [Reinforcement Learning from Imperfect Demonstrations](https://mlsanity.com/paper/1802.05313/) (2018)
- [Control of Memory, Active Perception, and Action in Minecraft](https://mlsanity.com/paper/1605.09128/) (2016)
- [Gotta Learn Fast: A New Benchmark for Generalization in RL](https://mlsanity.com/paper/1804.03720/) (2018)
- [ChainerRL: A Deep Reinforcement Learning Library](https://mlsanity.com/paper/1912.03905/) (2019)
- [The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/1904.10079/) (2019)
- [Generative Design in Minecraft (GDMC), Settlement Generation Competition](https://mlsanity.com/paper/1803.09853/) (2018)
- [Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning](https://mlsanity.com/paper/2003.05012/) (2020)
- [Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning](https://mlsanity.com/paper/1709.04083/) (2017)
- [The Multi-Agent Reinforcement Learning in Malm"O (MARL"O) Competition](https://mlsanity.com/paper/1901.08129/) (2019)
- [Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning](https://mlsanity.com/paper/1804.00198/) (2018)
- [Guaranteeing Reproducibility in Deep Learning Competitions](https://mlsanity.com/paper/2005.06041/) (2020)

## Built on by (most cited first, 30 of 41)

- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [Benchmarking the Spectrum of Agent Capabilities](https://mlsanity.com/paper/2109.06780/) (2021)
- [JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models](https://mlsanity.com/paper/2311.05997/) (2023)
- [SmartPlay: A Benchmark for LLMs as Intelligent Agents](https://mlsanity.com/paper/2310.01557/) (2023)
- [MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research](https://mlsanity.com/paper/2109.13202/) (2021)
- [Automated Reinforcement Learning (AutoRL): A Survey and Open Problems](https://mlsanity.com/paper/2201.03916/) (2022)
- [XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX](https://mlsanity.com/paper/2312.12044/) (2023)
- [Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction](https://mlsanity.com/paper/2301.10034/) (2023)
- [CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents](https://mlsanity.com/paper/2110.10067/) (2021)
- [JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2112.04907/) (2021)
- [The MineRL BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2107.01969/) (2021)
- [Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark](https://mlsanity.com/paper/2103.15332/) (2021)
- [S-Agents: Self-organizing Agents in Open-ended Environments](https://mlsanity.com/paper/2402.04578/) (2024)
- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [The Neural MMO Platform for Massively Multiagent Research](https://mlsanity.com/paper/2110.07594/) (2021)
- [DreamCraft: Text-Guided Generation of Functional 3D Environments in Minecraft](https://mlsanity.com/paper/2404.15538/) (2024)
- [Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization](https://mlsanity.com/paper/2403.10967/) (2024)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
- [ADAM: An Embodied Causal Agent in Open-World Environments](https://mlsanity.com/paper/2410.22194/) (2024)
- [WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games](https://mlsanity.com/paper/2605.17637/) (2026)
- [SPRING: Studying the Paper and Reasoning to Play Games](https://mlsanity.com/paper/2305.15486/) (2023)
- [Neural MMO 2.0: A Massively Multi-task Addition to Massively Multi-agent Learning](https://mlsanity.com/paper/2311.03736/) (2023)
- [Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning](https://mlsanity.com/paper/2207.11584/) (2022)
- [Accurate and Efficient World Modeling with Masked Latent Transformers](https://mlsanity.com/paper/2507.04075/) (2025)
- [Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation](https://mlsanity.com/paper/2107.02457/) (2021)
- [Unsupervised Skill-Discovery and Skill-Learning in Minecraft](https://mlsanity.com/paper/2107.08398/) (2021)
- [Defining a New NLP Playground](https://mlsanity.com/paper/2310.20633/) (2023)
- [Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft](https://mlsanity.com/paper/2112.03482/) (2021)

## Abstract (arXiv)

Although deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples, affording only a shrinking segment of the AI community access to their development. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we propose this second iteration of the MineRL Competition. 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, participants compete under a limited environment sample-complexity budget to develop systems which solve the MineRL ObtainDiamond task in Minecraft, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures and shaders. At the end of each round, competitors submit containerized versions of their learning algorithms to the AIcrowd platform where they are trained from scratch on a hold-out dataset-environment pair for a total of 4-days on a pre-specified hardware platform. In this follow-up iteration to the NeurIPS 2019 MineRL Competition, we implement new features to expand the scale and reach of the competition. In response to the feedback of the previous participants, we introduce a second minor track focusing on solutions without access to environment interactions of any kind except during test-time. Further we aim to prompt domain agnostic submissions by implementing several novel competition mechanics including action-space randomization and desemantization of observations and actions.