# Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft

arXiv: https://arxiv.org/abs/2003.06066 · HTML: https://mlsanity.com/paper/2003.06066/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 15 of 15)

- [Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos](https://mlsanity.com/paper/2206.11795/) (2022)
- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2112.04907/) (2021)
- [SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition](https://mlsanity.com/paper/2111.08857/) (2021)
- [Benchmarking End-to-End Behavioural Cloning on Video Games](https://mlsanity.com/paper/2004.00981/) (2020)
- [VIEW: Visual Imitation Learning with Waypoints](https://mlsanity.com/paper/2404.17906/) (2024)
- [LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence](https://mlsanity.com/paper/2405.17424/) (2024)
- [Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions](https://mlsanity.com/paper/2307.10524/) (2023)
- [Robust Visual Imitation Learning with Inverse Dynamics Representations](https://mlsanity.com/paper/2310.14274/) (2023)
- [Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates](https://mlsanity.com/paper/2310.17786/) (2023)
- [Self-Imitation Learning from Demonstrations](https://mlsanity.com/paper/2203.10905/) (2022)
- [Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning](https://mlsanity.com/paper/2212.12767/) (2022)
- [Towards robust and domain agnostic reinforcement learning competitions](https://mlsanity.com/paper/2106.03748/) (2021)
- [Teacher-student curriculum learning for reinforcement learning](https://mlsanity.com/paper/2210.17368/) (2022)
- [Explaining Imitation Learning through Frames](https://mlsanity.com/paper/2301.01088/) (2023)

## Abstract (arXiv)

Sample inefficiency of deep reinforcement learning methods is a major obstacle for their use in real-world applications. In this work, we show how human demonstrations can improve final performance of agents on the Minecraft minigame ObtainDiamond with only 8M frames of environment interaction. We propose a training procedure where policy networks are first trained on human data and later fine-tuned by reinforcement learning. Using a policy exploitation mechanism, experience replay and an additional loss against catastrophic forgetting, our best agent was able to achieve a mean score of 48. Our proposed solution placed 3rd in the NeurIPS MineRL Competition for Sample-Efficient Reinforcement Learning.