Awesome

Building Open-Ended Embodied Agents with Internet-Scale Knowledge

• Installation
• Getting Started
• Reference
• License

Installation

The code is developed upon Minedojo , which requires Python ≥ 3.9. We have tested on Ubuntu 20.04. Please follow this guide to install the prerequisites first, such as JDK 8 for running Minecraft backend. We highly recommend creating a new Conda virtual env to isolate dependencies.

Then install the simulator, run:

git clone https://github.com/CraftJarvis/MC-Simulator && cd MC-Simulator
pip install -e .

You can run the script below to verify the installation. It takes a while to compile the Java code for the first time. After that you should see a Minecraft window pop up, with the same gaming interface that human players receive. You should see the message [INFO] Installation Success if everything goes well.

python minedojo/scripts/validate_install.py

Note that if you are on a headless machine, don't forget to prepend either xvfb-run or MINEDOJO_HEADLESS=1:

xvfb-run python minedojo/scripts/validate_install.py
# --- OR ---
MINEDOJO_HEADLESS=1 python minedojo/scripts/validate_install.py

Getting Started

MineDojo provides a Gym-style interface for developing embodied agents that interact with the simulator in a loop. Here is a very simple code snippet of a hardcoded agent that runs forward and jumps every 10 steps in the "Harvest Wool" task:

import minedojo

env = minedojo.make(
    task_id="harvest_wool_with_shears_and_sheep",
    image_size=(160, 256)
)
obs = env.reset()
for i in range(50):
    act = env.action_space.no_op()
    act[0] = 1    # forward/backward
    if i % 10 == 0:
        act[2] = 1    # jump
    obs, reward, done, info = env.step(act)
env.close()

Please refer to this tutorial for a detailed walkthrough of your first agent. MineDojo features a multimodal observation space (RGB, compass, voxels, etc.) and a compound action space (movement, camera, attack, craft, etc.). See this doc to learn more. We recommend you to reference the full observation and action space specifications.

MineDojo can be extensively customized to be tailored to your research needs. Please check out customization guides on tasks, simulation, and privileged observation.

Reference

The simulator is developed upon Minedojo. If you find the simulator useful, please consider citing minedojo.

@article{fan2022minedojo,
  title   = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge},
  author  = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar},
  year    = {2022},
  journal = {arXiv preprint arXiv: Arxiv-2206.08853}
}

License