Massively Multitask Benchmarking Suite

MineDojo is a new framework built on the popular Minecraft game for embodied agent research. MineDojo features a simulation suite with 1000s of open-ended and language-prompted tasks, where the AI agents can freely explore a procedurally generated 3D world with diverse terrains to roam, materials to mine, tools to craft, structures to build, and wonders to discover.

Open-ended Exploration in Overworld, The Nether, and The End

Wide Variety of Terrains, Weathers, and Items

Diverse and Creative Tool Usage

Internet-scale Knowledge Base

Minecraft has more than 100M active players, who have collectively generated an enormous wealth of data. MineDojo features a massive database collected automatically from the internet. AI agents can learn from this treasure trove of knowledge to harvest actionable insights, acquire diverse skills, develop complex strategies, and discover interesting objectives to pursue. All our databases are open-access and available to download today! Click on each card below to find out more.

Quick Start

The code snippet below is a quick demo of how to run your first agent. MineDojo is extensively documented . Please follow the installation guide and tutorial to get started. Feel free to star and watch our GitHub repo  for future project updates 🥳!

  •                         import minedojo
    
    env = minedojo.make(
        task_id="harvest_wool_with_shears_and_sheep",
        image_size=(288, 512)
    )
    obs = env.reset()
    for i in range(60):
        act = env.action_space.no_op()
        act[0] = 1    # forward/backward
        if i % 50 == 0:
            act[2] = 1    # jump
        obs, rwd, done, info = env.step(act)
    env.close()
                        

Team

Email lead developers.   * Equal contribution.   † Equal advising.

Check out our paper!

@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}
}

MineDojo team ©2022