Official codebase for the paper "Odyssey: Empowering Minecraft Agents with Open-World Skills". This codebase is based on the Voyager framework.
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.
[Oct 1, 2024]🔥 We have additionally compared more baselines (with different open-sourced LLMs and agents) and designed more test scenarios (for the long-term planning task and the dynamic-immediate planning task) in the updated version of the paper.[Sep 1, 2024]🔥 We have additionally open-sourced the Web Crawler Program, which was used to collect data from Minecraft Wikis. Researchers can modify this program to crawl data relevant to their needs.[Aug 14, 2024]🔥 We have additionally open-sourced the Comprehensive Skill Library, aiming to provide an automated tool to collect all collectible and craftable items in Minecraft.[Jul 23, 2024]🔥 The paper for ODYSSEY has been uploaded to arXiv![Jun 13, 2024]🔥 The GitHub repository for ODYSSEY has been open-sourced!
All demonstration videos were captured using the spectator mode within Minecraft. To comply with GitHub's file size restrictions, some videos have been accelerated.
Mining Diamonds from Scratch:
Craft Sword and Combat Zombie:
Shear a Sheep and Milk a Cow:
Autonomous Exploration: (Only First Few Rounds)
-
LLM-Backend
Code to deploy LLM backend.
-
MC-Crawler
Crawling Minecraft game information from Minecraft Wiki and storing data in markdown format.
-
MineMA-Model-Fine-Tuning
Code to fine-tune the LLaMa model and generate training and test datasets.
-
Odyssey
Code for Minecraft agents based on a large language model and skill library.
We use Python ≥ 3.9 and Node.js ≥ 16.13.0. We have tested on Ubuntu 20.04, Windows 10, and macOS.
cd Odyssey
pip install -e .
pip install -r requirements.txtnpm install -g yarn
cd Odyssey/odyssey/env/mineflayer
yarn install
cd Odyssey/odyssey/env/mineflayer/mineflayer-collectblock
npx tsc
cd Odyssey/odyssey/env/mineflayer
yarn install
cd Odyssey/odyssey/env/mineflayer/node_modules/mineflayer-collectblock
npx tscYou can deploy a Minecraft server using docker. See here.
-
Need to install git-lfs first.
-
Download the embedding model repository
git lfs install git clone https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2.git
-
The directory where you clone the repository is then used to set
embedding_dir.
You need to create config.json according to the format of conf/config.json.keep.this in conf directory.
server_host: LLaMa backend server ip.server_port: LLaMa backend server port.NODE_SERVER_PORT: Node service port.SENTENT_EMBEDDING_DIR: Path to your embedding model.MC_SERVER_HOST: Minecraft server ip.MC_SERVER_PORT: Minecraft server port.
def test_subgoal():
odyssey_l3_8b = Odyssey(
mc_port=mc_port,
mc_host=mc_host,
env_wait_ticks=env_wait_ticks,
skill_library_dir="./skill_library",
reload=True, # set to True if the skill_json updated
embedding_dir=embedding_dir, # your model path
environment='subgoal',
resume=False,
server_port=node_port,
critic_agent_model_name = ModelType.LLAMA3_8B_V3,
comment_agent_model_name = ModelType.LLAMA3_8B_V3,
curriculum_agent_qa_model_name = ModelType.LLAMA3_8B_V3,
curriculum_agent_model_name = ModelType.LLAMA3_8B_V3,
action_agent_model_name = ModelType.LLAMA3_8B_V3,
)
# 5 classic MC tasks
test_sub_goals = ["craft crafting table", "craft wooden pickaxe", "craft stone pickaxe", "craft iron pickaxe", "mine diamond"]
try:
odyssey_l3_8b.inference_sub_goal(task="subgoal_llama3_8b_v3", sub_goals=test_sub_goals)
except Exception as e:
print(e)| Model | For what |
|---|---|
| action_agent_model_name | Choose one of the k retrieved skills to execute |
| curriculum_agent_model_name | Propose tasks for farming and explore |
| curriculum_agent_qa_model_name | Schedule subtasks for combat, generate QA context, and rank the order to kill monsters |
| critic_agent_model_name | Action critic |
| comment_agent_model_name | Give the critic about the last combat result, in order to reschedule subtasks for combat |
def test_combat():
odyssey_l3_70b = Odyssey(
mc_port=mc_port,
mc_host=mc_host,
env_wait_ticks=env_wait_ticks,
skill_library_dir="./skill_library",
reload=True, # set to True if the skill_json updated
embedding_dir=embedding_dir, # your model path
environment='combat',
resume=False,
server_port=node_port,
critic_agent_model_name = ModelType.LLAMA3_70B_V1,
comment_agent_model_name = ModelType.LLAMA3_70B_V1,
curriculum_agent_qa_model_name = ModelType.LLAMA3_70B_V1,
curriculum_agent_model_name = ModelType.LLAMA3_70B_V1,
action_agent_model_name = ModelType.LLAMA3_70B_V1,
)
multi_rounds_tasks = ["1 enderman", "3 zombie"]
l70_v1_combat_benchmark = [
# Single-mob tasks
"1 skeleton", "1 spider", "1 zombified_piglin", "1 zombie",
# Multi-mob tasks
"1 zombie, 1 skeleton", "1 zombie, 1 spider", "1 zombie, 1 skeleton, 1 spider"
]
for task in l70_v1_combat_benchmark:
odyssey_l3_70b.inference(task=task, reset_env=False, feedback_rounds=1)
for task in multi_rounds_tasks:
odyssey_l3_70b.inference(task=task, reset_env=False, feedback_rounds=3)def test_farming():
odyssey_l3_8b = Odyssey(
mc_port=mc_port,
mc_host=mc_host,
env_wait_ticks=env_wait_ticks,
skill_library_dir="./skill_library",
reload=True, # set to True if the skill_json updated
embedding_dir=embedding_dir, # your model path
environment='farming',
resume=False,
server_port=node_port,
critic_agent_model_name = ModelType.LLAMA3_8B_V3,
comment_agent_model_name = ModelType.LLAMA3_8B_V3,
curriculum_agent_qa_model_name = ModelType.LLAMA3_8B_V3,
curriculum_agent_model_name = ModelType.LLAMA3_8B_V3,
action_agent_model_name = ModelType.LLAMA3_8B_V3,
)
farming_benchmark = [
# Single-goal tasks
"collect 1 wool by shearing 1 sheep",
"collect 1 bucket of milk",
"cook 1 meat (beef or mutton or pork or chicken)",
# Multi-goal tasks
"collect and plant 1 seed (wheat or melon or pumpkin)"
]
for goal in farming_benchmark:
odyssey_l3_8b.learn(goals=goal, reset_env=False)def explore():
odyssey_l3_8b = Odyssey(
mc_port=mc_port,
mc_host=mc_host,
env_wait_ticks=env_wait_ticks,
skill_library_dir="./skill_library",
reload=True, # set to True if the skill_json updated
embedding_dir=embedding_dir, # your model path
environment='explore',
resume=False,
server_port=node_port,
critic_agent_model_name = ModelType.LLAMA3_8B,
comment_agent_model_name = ModelType.LLAMA3_8B,
curriculum_agent_qa_model_name = ModelType.LLAMA3_8B,
curriculum_agent_model_name = ModelType.LLAMA3_8B,
action_agent_model_name = ModelType.LLAMA3_8B,
username='bot1_8b'
)
odyssey_l3_8b.learn()| ID | Paper | Authors | Venue |
|---|---|---|---|
| 1 | MineRL: A Large-Scale Dataset of Minecraft Demonstrations | William H. Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, Ruslan Salakhutdinov | IJCAI 2019 |
| 2 | Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos | Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune | arXiv 2022 |
| 3 | MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge | Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar | NeurIPS 2022 |
| 4 | Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction | Shaofei Cai, Zihao Wang, Xiaojian Ma, Anji Liu, Yitao Liang | CVPR 2023 |
| 5 | Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents | Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, Yitao Liang | NeurIPS 2023 |
| 6 | Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks | Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie, Penglin Cai, Hao Dong, Zongqing Lu | NeurIPS Workshop 2023 |
| 7 | Voyager: An Open-Ended Embodied Agent with Large Language Models | Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar | arXiv 2023 |
| 8 | Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory | Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, Yu Qiao, Zhaoxiang Zhang, Jifeng Dai | arXiv 2023 |
| 9 | STEVE-1: A Generative Model for Text-to-Behavior in Minecraft | Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, Sheila McIlraith | NeurIPS 2023 |
| 10 | GROOT: Learning to Follow Instructions by Watching Gameplay Videos | Shaofei Cai, Bowei Zhang, Zihao Wang, Xiaojian Ma, Anji Liu, Yitao Liang | arXiv 2023 |
| 11 | MCU: A Task-centric Framework for Open-ended Agent Evaluation in Minecraft | Haowei Lin, Zihao Wang, Jianzhu Ma, Yitao Liang | arXiv 2023 |
| 12 | LLaMA Rider: Spurring Large Language Models to Explore the Open World | Yicheng Feng, Yuxuan Wang, Jiazheng Liu, Sipeng Zheng, Zongqing Lu | arXiv 2023 |
| 13 | JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models | Zihao Wang, Shaofei Cai, Anji Liu, Yonggang Jin, Jinbing Hou, Bowei Zhang, Haowei Lin, Zhaofeng He, Zilong Zheng, Yaodong Yang, Xiaojian Ma, Yitao Liang | arXiv 2023 |
| 14 | See and Think: Embodied Agent in Virtual Environment | Zhonghan Zhao, Wenhao Chai, Xuan Wang, Li Boyi, Shengyu Hao, Shidong Cao, Tian Ye, Jenq-Neng Hwang, Gaoang Wang | arXiv 2023 |
| 15 | Creative Agents: Empowering Agents with Imagination for Creative Tasks | Chi Zhang, Penglin Cai, Yuhui Fu, Haoqi Yuan, Zongqing Lu | arXiv 2023 |
| 16 | MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception | Yiran Qin, Enshen Zhou, Qichang Liu, Zhenfei Yin, Lu Sheng, Ruimao Zhang, Yu Qiao, Jing Shao | arXiv 2024 |
| 17 | Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft | Hao Li, Xue Yang, Zhaokai Wang, Xizhou Zhu, Jie Zhou, Yu Qiao, Xiaogang Wang, Hongsheng Li, Lewei Lu, Jifeng Dai | arXiv 2024 |
If you find this work useful for your research, please cite our paper:
@article{Odyssey2024,
title={Odyssey: Empowering Agents with Open-World Skills},
author={Shunyu Liu and Yaoru Li and Kongcheng Zhang and Zhenyu Cui and Wenkai Fang and Yuxuan Zheng and Tongya Zheng and Mingli Song},
journal={arXiv preprint arXiv:2407.15325},
year={2024}
}
| Component | License |
|---|---|
| Codebase | MIT License |
| Minecraft Q&A Dataset | Creative Commons Attribution Non Commercial Share Alike 3.0 Unported (CC BY-NC-SA 3.0) |
This project is developed by VIPA Lab from Zhejiang University. Please feel free to contact me via email ([email protected]) if you are interested in our research :)
