# Playing Minecraft with Behavioural Cloning

arXiv: https://arxiv.org/abs/2005.03374 · HTML: https://mlsanity.com/paper/2005.03374/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 8 of 8)

- [JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2112.04907/) (2021)
- [Zero-shot Imitation Policy via Search in Demonstration Dataset](https://mlsanity.com/paper/2401.16398/) (2024)
- [Beyond the Sum: Unlocking AI Agents Potential Through Market Forces](https://mlsanity.com/paper/2501.10388/) (2024)
- [Behavioral Cloning via Search in Embedded Demonstration Dataset](https://mlsanity.com/paper/2306.09082/) (2023)
- [Improving Behavioural Cloning with Human-Driven Dynamic Dataset Augmentation](https://mlsanity.com/paper/2201.07719/) (2022)
- [Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode](https://mlsanity.com/paper/2410.23419/) (2024)
- [VRScout: Towards Real-Time, Autonomous Testing of Virtual Reality Games](https://mlsanity.com/paper/2511.00002/) (2025)
- [Unsupervised Hierarchical Skill Discovery](https://mlsanity.com/paper/2601.23156/) (2026)

## Abstract (arXiv)

MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.