# Reinforcement Learning Friendly Vision-Language Model for Minecraft

arXiv: https://arxiv.org/abs/2303.10571 · HTML: https://mlsanity.com/paper/2303.10571/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://mlsanity.com/paper/2109.14084/) (2021)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos](https://mlsanity.com/paper/2206.11795/) (2022)
- [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)
- [Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling](https://mlsanity.com/paper/2301.12050/) (2023)
- [HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval](https://mlsanity.com/paper/2103.15049/) (2021)
- [MDMMT: Multidomain Multimodal Transformer for Video Retrieval](https://mlsanity.com/paper/2103.10699/) (2021)
- [Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1712.07294/) (2017)
- [Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction](https://mlsanity.com/paper/2301.10034/) (2023)
- [JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2112.04907/) (2021)
- [The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors](https://mlsanity.com/paper/1904.10079/) (2019)
- [MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned](https://mlsanity.com/paper/2202.10583/) (2022)
- [Retrospective on the 2021 BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2204.07123/) (2022)

## Built on by (most cited first, 14 of 14)

- [MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception](https://mlsanity.com/paper/2312.07472/) (2023)
- [Vision-Language Models as a Source of Rewards](https://mlsanity.com/paper/2312.09187/) (2023)
- [Code as Reward: Empowering Reinforcement Learning with VLMs](https://mlsanity.com/paper/2402.04764/) (2024)
- [Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks](https://mlsanity.com/paper/2303.16563/) (2023)
- [MCU: An Evaluation Framework for Open-Ended Game Agents](https://mlsanity.com/paper/2310.08367/) (2023)
- [Creative Agents: Empowering Agents with Imagination for Creative Tasks](https://mlsanity.com/paper/2312.02519/) (2023)
- [Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models](https://mlsanity.com/paper/2506.12822/) (2025)
- [Learning from Visual Observation via Offline Pretrained State-to-Go Transformer](https://mlsanity.com/paper/2306.12860/) (2023)
- [Position: Interactive Generative Video as Next-Generation Game Engine](https://mlsanity.com/paper/2503.17359/) (2025)
- [MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents](https://mlsanity.com/paper/2410.03450/) (2024)
- [Open-World Skill Discovery from Unsegmented Demonstrations](https://mlsanity.com/paper/2503.10684/) (2025)
- [GuessBench: Sensemaking Multimodal Creativity in the Wild](https://mlsanity.com/paper/2506.00814/) (2025)
- [VITA: Zero-Shot Value Functions via Test-Time Adaptation of Vision-Language Models](https://mlsanity.com/paper/2506.10085/) (2025)
- [VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning](https://mlsanity.com/paper/2607.00483/) (2026)

## Abstract (arXiv)

One of the essential missions in the AI research community is to build an autonomous embodied agent that can achieve high-level performance across a wide spectrum of tasks. However, acquiring or manually designing rewards for all open-ended tasks is unrealistic. In this paper, we propose a novel cross-modal contrastive learning framework architecture, CLIP4MC, aiming to learn a reinforcement learning (RL) friendly vision-language model (VLM) that serves as an intrinsic reward function for open-ended tasks. Simply utilizing the similarity between the video snippet and the language prompt is not RL-friendly since standard VLMs may only capture the similarity at a coarse level. To achieve RL-friendliness, we incorporate the task completion degree into the VLM training objective, as this information can assist agents in distinguishing the importance between different states. Moreover, we provide neat YouTube datasets based on the large-scale YouTube database provided by MineDojo. Specifically, two rounds of filtering operations guarantee that the dataset covers enough essential information and that the video-text pair is highly correlated. Empirically, we demonstrate that the proposed method achieves better performance on RL tasks compared with baselines. The code and datasets are available at https://github.com/PKU-RL/CLIP4MC.