Summary of Odyssey: Empowering Minecraft Agents with Open-world Skills, by Shunyu Liu et al.
Odyssey: Empowering Minecraft Agents with Open-World Skills
by Shunyu Liu, Yaoru Li, Kongcheng Zhang, Zhenyu Cui, Wenkai Fang, Yuxuan Zheng, Tongya Zheng, Mingli Song
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new framework called Odyssey that enables Large Language Model (LLM)-based agents to explore the vast Minecraft world. The framework consists of three key parts: an interactive agent with open-world skills, a fine-tuned LLaMA-3 model trained on a large question-answering dataset, and a new agent capability benchmark. The authors demonstrate that Odyssey can effectively evaluate different capabilities of LLM-based agents, including long-term planning, dynamic-immediate planning, and autonomous exploration tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers created a new way for computer programs to explore the Minecraft world. They designed a special framework called Odyssey that helps these programs learn and make decisions in the game. The framework has three parts: an agent that can perform different actions, a special kind of AI model trained on questions about Minecraft, and a set of challenges to test how well the program can do certain tasks. This research could lead to more advanced computer programs that can play games or solve problems. |
Keywords
» Artificial intelligence » Large language model » Llama » Question answering
