---
title: "Odyssey : Empowering Minecraft Agents with Open-World Skills"
authors:
  - "Shunyu Liu"
  - "Yaoru Li"
  - "Kongcheng Zhang"
  - "Zhenyu Cui"
  - "Wenkai Fang"
  - "Yuxuan Zheng"
  - "Tongya Zheng"
  - "Mingli Song"
doi: "10.24963/ijcai.2025/22"
url: "https://ijcai.org/proceedings/2025/22"
pdf: "https://ijcai.org/proceedings/2025/0022.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "187-195"
track: "Main Track"
keywords:
  - "Natural Language Processing: NLP: Applications"
  - "Agent-based and Multi-agent Systems: MAS: Applications"
  - "Agent-based and Multi-agent Systems: MAS: Agent-based simulation and emergence"
---

# Odyssey : Empowering Minecraft Agents with Open-World Skills

Shunyu Liu, Yaoru Li, Kongcheng Zhang, Zhenyu Cui, Wenkai Fang, Yuxuan Zheng, Tongya Zheng, Mingli Song

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 187-195. DOI: [10.24963/ijcai.2025/22](https://doi.org/10.24963/ijcai.2025/22)

## Abstract

Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving basic programmatic tasks, e.g., material collection and tool-crafting following the Minecraft tech-tree, treating the ObtainDiamond task as the ultimate goal. This limitation stems from the narrowly defined set of actions available to agents, requiring them to learn effective long-horizon strategies from scratch. Consequently, discovering diverse gameplay opportunities in the open world becomes challenging. In this work, we introduce Odyssey, a new framework that empowers Large Language Model (LLM)-based agents with open-world skills to explore the vast Minecraft world. Odyssey comprises three key parts: (1) An interactive agent with an open-world skill library that consists of 40 primitive skills and 183 compositional skills. (2) A fine-tuned LLaMA-3 model trained on a large question-answering dataset with 390k+ instruction entries derived from the Minecraft Wiki. (3) A new agent capability benchmark includes the long-term planning task, the dynamic-immediate planning task, and the autonomous exploration task. Extensive experiments demonstrate that the proposed Odyssey framework can effectively evaluate different capabilities of LLM-based agents. All datasets, model weights, and code are publicly available to motivate future research on more advanced autonomous agent solutions.