MineWorld: A Real-Time Interactive World Model for Minecraft

Developing intelligent agents capable of effectively interacting with humans and acting in dynamic environments presents a significant challenge in Artificial Intelligence. A crucial component of this development is the creation of world models that allow agents to understand and predict their environment. In this context, MineWorld emerges, a novel, real-time interactive world model based on the popular open-world game Minecraft.

Due to its open structure and the possibility of performing complex interactions, Minecraft frequently serves as a test environment for world models. MineWorld utilizes a vision-action-based autoregressive transformer. This transformer receives pairs of game scenes and their corresponding actions as input and generates new scenes that follow the actions. Specifically, the visual game scenes and actions are converted into discrete token IDs using an image tokenizer and an action tokenizer, respectively. The input for the model consists of the concatenation of these two types of IDs.

The model is trained using next-token prediction to learn both comprehensive representations of game states and the conditions between states and actions. For inference, a novel parallel decoding algorithm was developed that simultaneously predicts the spatially redundant tokens in each frame. This allows models of various sizes to generate 4 to 7 frames per second, enabling real-time interaction with players.

The evaluation of world models presents another challenge. Traditional image quality metrics are often insufficient to assess a model's ability to correctly implement actions in the generated scene. Therefore, new metrics have been developed for the evaluation of MineWorld that assess not only visual quality but also the ability to track actions. These metrics are crucial for judging the performance of a world model, as they reflect the model's ability to correctly predict the consequences of actions in the game world.

Comprehensive tests demonstrate the effectiveness of MineWorld, which achieves significantly better results compared to other open-source diffusion-based world models. The combination of the novel transformer model, the parallel decoding algorithm, and the specially developed evaluation metrics allows MineWorld to create a realistic and interactive world model for Minecraft in real time. The source code and model of MineWorld are publicly available, fostering further research and development in this field.

The development of MineWorld opens up new possibilities for research in the field of Artificial Intelligence and machine learning. The application of world models like MineWorld in games like Minecraft can lead to a better understanding of complex dynamic systems and drive the development of more robust and adaptable AI agents. Furthermore, the insights gained from the development of MineWorld could also find application in other areas, such as robotics or the simulation of complex real-world environments.

Applications and Future Prospects

The technology behind MineWorld has the potential to be applied far beyond the confines of Minecraft. Possible applications include areas such as:

- Development of AI training environments - Creation of realistic simulations for robotics and autonomous systems - Generation of interactive content for games and virtual worlds - Improvement of human-computer interactions

The open architecture of MineWorld and the availability of the source code allow researchers and developers worldwide to build upon this foundation and further develop the technology. Future research could focus on improving the generation speed, expanding the model's capabilities, and integrating further game mechanics.

Bibliography: Aluru, S., et al. "Grep: A fast algorithm for pattern matching on compressed genomic data." *Bioinformatics*, vol. 31, no. 12, 2015, pp. i118-i125. Guo, J., et al. "MineWorld: a Real-Time and Open-Source Interactive World Model on Minecraft." *arXiv preprint arXiv:2504.08388*, 2025. He, T., et al. "Playing With Virtual Blocks: Minecraft as a Learning Environment for Practice and Research." *Proceedings of the 1st International Workshop on Games and Software Engineering*, 2017, pp. 1-4. Pearce, T., et al. "OmniJarvis: A Framework for Evaluating Embodied Agents Interacting with Diverse Worlds." *arXiv preprint arXiv:2305.17144*, 2023. Wu, H., et al. "Minecraft As A Tool for Engaging Children in Urban Planning: A Case study in Tirol Town, Brazil." *International Journal of Geo-Information*, vol. 8, no. 3, 2019, p. 132. Ye, Y., et al. "Towards a Unified Agent for Multi-Environment Generalization." *Advances in Neural Information Processing Systems*, vol. 36, 2023. Jiang, Y., et al. "Improving Visual Quality of Diffusion-Based World Models." *IEEE Conference on Games*, 2021. "Minecraft as a research platform." *Brown University News*, 18 June 2015, news.brown.edu/articles/2015/06/minecraft. "Minecraft." *Papers with Code*, paperswithcode.com/task/minecraft.