Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/MineDojo/Voyager

An Open-Ended Embodied Agent with Large Language Models
https://github.com/MineDojo/Voyager

embodied-learning large-language-models minecraft open-ended-learning

Last synced: 2 months ago
JSON representation

An Open-Ended Embodied Agent with Large Language Models

Lists

README 

        
# Voyager: An Open-Ended Embodied Agent with Large Language Models




[[Website]](https://voyager.minedojo.org/)

[[Arxiv]](https://arxiv.org/abs/2305.16291)

[[PDF]](https://voyager.minedojo.org/assets/documents/voyager.pdf)

[[Tweet]](https://twitter.com/DrJimFan/status/1662115266933972993?s=20)



[![Python Version](https://img.shields.io/badge/Python-3.9-blue.svg)](https://github.com/MineDojo/Voyager)

[![GitHub license](https://img.shields.io/github/license/MineDojo/Voyager)](https://github.com/MineDojo/Voyager/blob/main/LICENSE)

______________________________________________________________________



https://github.com/MineDojo/Voyager/assets/25460983/ce29f45b-43a5-4399-8fd8-5dd105fd64f2



![](images/pull.png)






We introduce Voyager, the first LLM-powered embodied lifelong learning agent

in Minecraft that continuously explores the world, acquires diverse skills, and

makes novel discoveries without human intervention. Voyager consists of three

key components: 1) an automatic curriculum that maximizes exploration, 2) an

ever-growing skill library of executable code for storing and retrieving complex

behaviors, and 3) a new iterative prompting mechanism that incorporates environment

feedback, execution errors, and self-verification for program improvement.

Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for

model parameter fine-tuning. The skills developed by Voyager are temporally

extended, interpretable, and compositional, which compounds the agent’s abilities

rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows

strong in-context lifelong learning capability and exhibits exceptional proficiency

in playing Minecraft. It obtains 3.3× more unique items, travels 2.3× longer

distances, and unlocks key tech tree milestones up to 15.3× faster than prior SOTA.

Voyager is able to utilize the learned skill library in a new Minecraft world to

solve novel tasks from scratch, while other techniques struggle to generalize.



In this repo, we provide Voyager code. This codebase is under [MIT License](LICENSE).



# Installation

Voyager requires Python ≥ 3.9 and Node.js ≥ 16.13.0. We have tested on Ubuntu 20.04, Windows 11, and macOS. You need to follow the instructions below to install Voyager.



## Python Install

```

git clone https://github.com/MineDojo/Voyager

cd Voyager

pip install -e .

```



## Node.js Install

In addition to the Python dependencies, you need to install the following Node.js packages:

```

cd voyager/env/mineflayer

npm install -g npx

npm install

cd mineflayer-collectblock

npx tsc

cd ..

npm install

```



## Minecraft Instance Install



Voyager depends on Minecraft game. You need to install Minecraft game and set up a Minecraft instance.



Follow the instructions in [Minecraft Login Tutorial](installation/minecraft_instance_install.md) to set up your Minecraft Instance.



## Fabric Mods Install



You need to install fabric mods to support all the features in Voyager. Remember to use the correct Fabric version of all the mods. 



Follow the instructions in [Fabric Mods Install](installation/fabric_mods_install.md) to install the mods.



# Getting Started

Voyager uses OpenAI's GPT-4 as the language model. You need to have an OpenAI API key to use Voyager. You can get one from [here](https://platform.openai.com/account/api-keys).



After the installation process, you can run Voyager by:

```python

from voyager import Voyager



# You can also use mc_port instead of azure_login, but azure_login is highly recommended

azure_login = {

    "client_id": "YOUR_CLIENT_ID",

    "redirect_url": "https://127.0.0.1/auth-response",

    "secret_value": "[OPTIONAL] YOUR_SECRET_VALUE",

    "version": "fabric-loader-0.14.18-1.19", # the version Voyager is tested on

}

openai_api_key = "YOUR_API_KEY"



voyager = Voyager(

    azure_login=azure_login,

    openai_api_key=openai_api_key,

)



# start lifelong learning

voyager.learn()

```



* If you are running with `Azure Login` for the first time, it will ask you to follow the command line instruction to generate a config file.

* For `Azure Login`, you also need to select the world and open the world to LAN by yourself. After you run `voyager.learn()` the game will pop up soon, you need to:

  1. Select `Singleplayer` and press `Create New World`.

  2. Set Game Mode to `Creative` and Difficulty to `Peaceful`.

  3. After the world is created, press `Esc` key and press `Open to LAN`.

  4. Select `Allow cheats: ON` and press `Start LAN World`. You will see the bot join the world soon. 



# Resume from a checkpoint during learning



If you stop the learning process and want to resume from a checkpoint later, you can instantiate Voyager by:

```python

from voyager import Voyager



voyager = Voyager(

    azure_login=azure_login,

    openai_api_key=openai_api_key,

    ckpt_dir="YOUR_CKPT_DIR",

    resume=True,

)

```



# Run Voyager for a specific task with a learned skill library



If you want to run Voyager for a specific task with a learned skill library, you should first pass the skill library directory to Voyager:

```python

from voyager import Voyager



# First instantiate Voyager with skill_library_dir.

voyager = Voyager(

    azure_login=azure_login,

    openai_api_key=openai_api_key,

    skill_library_dir="./skill_library/trial1", # Load a learned skill library.

    ckpt_dir="YOUR_CKPT_DIR", # Feel free to use a new dir. Do not use the same dir as skill library because new events will still be recorded to ckpt_dir. 

    resume=False, # Do not resume from a skill library because this is not learning.

)

```

Then, you can run task decomposition. Notice: Occasionally, the task decomposition may not be logical. If you notice the printed sub-goals are flawed, you can rerun the decomposition.

```python

# Run task decomposition

task = "YOUR TASK" # e.g. "Craft a diamond pickaxe"

sub_goals = voyager.decompose_task(task=task)

```

Finally, you can run the sub-goals with the learned skill library:

```python

voyager.inference(sub_goals=sub_goals)

```



For all valid skill libraries, see [Learned Skill Libraries](skill_library/README.md).



# FAQ

If you have any questions, please check our [FAQ](FAQ.md) first before opening an issue.



# Paper and Citation



If you find our work useful, please consider citing us! 



```bibtex

@article{wang2023voyager,

  title   = {Voyager: An Open-Ended Embodied Agent with Large Language Models},

  author  = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},

  year    = {2023},

  journal = {arXiv preprint arXiv: Arxiv-2305.16291}

}

```



Disclaimer: This project is strictly for research purposes, and not an official product from NVIDIA.