# Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.05012 · HTML: https://mlsanity.com/paper/2003.05012/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 18 of 18)

- [Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory](https://mlsanity.com/paper/2305.17144/) (2023)
- [Benchmarking the Spectrum of Agent Capabilities](https://mlsanity.com/paper/2109.06780/) (2021)
- [The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/2101.11071/) (2021)
- [Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks](https://mlsanity.com/paper/2303.16563/) (2023)
- [Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods](https://mlsanity.com/paper/2102.13045/) (2021)
- [MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned](https://mlsanity.com/paper/2202.10583/) (2022)
- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [SymbolicAI: A framework for logic-based approaches combining generative models and solvers](https://mlsanity.com/paper/2402.00854/) (2024)
- [Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter](https://mlsanity.com/paper/2208.03374/) (2022)
- [Scaling Imitation Learning in Minecraft](https://mlsanity.com/paper/2007.02701/) (2020)
- [Distilling Reinforcement Learning Tricks for Video Games](https://mlsanity.com/paper/2107.00703/) (2021)
- [Accelerating the Learning of TAMER with Counterfactual Explanations](https://mlsanity.com/paper/2108.01358/) (2021)
- [Towards robust and domain agnostic reinforcement learning competitions](https://mlsanity.com/paper/2106.03748/) (2021)
- [Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options](https://mlsanity.com/paper/2103.06757/) (2021)
- [Incorporating Rivalry in Reinforcement Learning for a Competitive Game](https://mlsanity.com/paper/2208.10327/) (2022)
- [Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO](https://mlsanity.com/paper/2308.15802/) (2023)
- [Academic competitions](https://mlsanity.com/paper/2312.00268/) (2023)
- [Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game](https://mlsanity.com/paper/2511.17925/) (2025)

## Abstract (arXiv)

To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019). The primary goal of this competition was to promote the development of algorithms that use human demonstrations alongside reinforcement learning to reduce the number of samples needed to solve complex, hierarchical, and sparse environments. We describe the competition, outlining the primary challenge, the competition design, and the resources that we provided to the participants. We provide an overview of the top solutions, each of which use deep reinforcement learning and/or imitation learning. We also discuss the impact of our organizational decisions on the competition and future directions for improvement.