MineStudio: A Streamlined Package for Minecraft AI Agent Development
Shaofei Cai, Zhancun Mu, Kaichen He, Bowei Zhang, Xinyue Zheng, Anji Liu, Yitao Liang
TL;DR
This paper introduces MineStudio, an open-source package that streamlines the development of autonomous Minecraft agents by integrating seven engineering components: simulator, data, model, offline pre-training, online fine-tuning, inference, and benchmarking. It details implementations such as a hook-based simulator, LMDB-backed trajectory data storage, a unified policy template with pre-built models, and scalable training/inference pipelines leveraging PyTorch Lightning and Ray. The framework also provides benchmarking tools and a comparative analysis against existing Minecraft frameworks, arguing that MineStudio reduces engineering overhead and accelerates algorithmic exploration. The work aims to advance embodied AI research in open-world, long-horizon tasks within Minecraft and to make rigorous RL experimentation more reproducible and accessible.
Abstract
Minecraft's complexity and diversity as an open world make it a perfect environment to test if agents can learn, adapt, and tackle a variety of unscripted tasks. However, the development and validation of novel agents in this setting continue to face significant engineering challenges. This paper presents MineStudio, an open-source software package designed to streamline the development of autonomous agents in Minecraft. MineStudio represents the first comprehensive integration of seven critical engineering components: simulator, data, model, offline pre-training, online fine-tuning, inference, and benchmark, thereby allowing users to concentrate their efforts on algorithm innovation. We provide a user-friendly API design accompanied by comprehensive documentation and tutorials. Our project is released at https://github.com/CraftJarvis/MineStudio.