MineRL
v0.4.4

Tutorials and Guides

  • Installation
  • Hello World: Your First Agent
    • Creating an environment
    • Taking actions
    • No-op actions and a better policy
  • Downloading and Sampling The Dataset
    • Introduction
    • Setting up environment variables
    • Downloading the MineRL Dataset with minerl.data.download
    • Sampling the Dataset with buffered_batch_iter
    • Moderate Human Demonstrations
  • K-means exploration
  • Visualizing The Data minerl.viewer
  • Interactive Mode minerl.interactor
  • Creating A Custom Environment
    • Introduction
    • Setup
    • Contruct the Environment Class
    • Modify the World
    • Set the Initial Agent Inventory
    • Create Reward Functionality
    • Construct a Quit Handler
    • Allow the Agent to Place Water
    • Give Extra Observations
    • Set the Time
    • Other Functions to Implement
    • Using the Environment
  • Using Minecraft Commands
    • Introduction
    • How Can MC Commands speed up training?
    • Adding the ChatAction to your envspec
    • Abstracted Command Sending
    • Advanced use

MineRL Environments

  • General Information
  • Environment Handlers
    • Environment Handlers
    • Spaces
      • Enum Spaces
    • Observations
      • Visual Observations - pov, third-person
      • Equip Observations - equipped_items
    • Actions
      • Camera Control - camera
      • Tool Control - equip and use
  • MineRL Diamond Competition Intro Track Environments
    • MineRLTreechop-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigate-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateDense-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateExtreme-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateExtremeDense-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainDiamond-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainDiamondDense-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainIronPickaxe-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainIronPickaxeDense-v0
      • Observation Space
      • Action Space
      • Usage
  • MineRL Diamond Competition Research Track Environments
    • MineRLTreechopVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateDenseVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateExtremeVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLNavigateExtremeDenseVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainDiamondVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainDiamondDenseVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainIronPickaxeVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
    • MineRLObtainIronPickaxeDenseVectorObf-v0
      • Observation Space
      • Action Space
      • Usage
  • MineRL BASALT Competition Environments
    • MineRLBasaltFindCave-v0
      • Observation Space
      • Action Space
      • Starting Inventory
      • Usage
    • MineRLBasaltMakeWaterfall-v0
      • Observation Space
      • Action Space
      • Starting Inventory
      • Usage
    • MineRLBasaltCreateVillageAnimalPen-v0
      • Observation Space
      • Action Space
      • Starting Inventory
      • Usage
    • MineRLBasaltBuildVillageHouse-v0
      • Observation Space
      • Action Space
      • Starting Inventory
      • Usage

Notes

  • Performance tips
    • Slowdown in obfuscated environments
    • Faster alternative to xvfb
  • Links to papers and projects
    • Presentations
    • MineRL papers
    • 2019 competitor code/papers
    • 2020 competitor code/papers
    • Other papers that use the MineRL environment
    • Other
  • Windows FAQ
    • The The system cannot find the path specified error (installing)
    • The freeze_support error (multiprocessing)

MineRL package API Reference

  • minerl.env
    • MineRLEnv
    • InstanceManager
  • minerl.data
    • MineRLv0
  • minerl.herobraine
    • Handlers
    • Agent Handlers
      • Agent Start Handlers
      • Agent Quit Handlers
      • Reward Handlers
      • Action Handlers
        • Camera
        • Craft
        • Equip
        • Keyboard
        • Place
        • Smelt
        • Chat
      • Observation Handlers
        • Compass
        • Damage Source
        • Equipped Item
        • Inventory
        • Lifestats
        • Location Stats
        • Base Stats
        • POV
    • Server Handlers
      • Server Start Handlers
      • Server Quit Handlers
      • World Handlers
MineRL
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  • Links to papers and projects
  • Edit on GitHub

Links to papers and projects

Here you can find useful links to the presentations, code and papers of the finalists in previous MineRL competitions, as well as other publications and projects that use MineRL.

To see all papers that cite MineRL, check Google Scholar. You can also create alerts there to get notified whenever a new citation appears.

If you want to add your paper/project here, do not hesitate to create a pull request in the main repository!

Presentations

  • MineRL 2019 - Finalists presentations at NeurIPS 2019

  • MineRL 2019 - 1st place winners presentation, longer one (slides in English, talk in Russian)

  • MineRL 2020 - Round 1 finalists presentations at NeurIPS 2020

  • MineRL 2020 - Round 2 finalists presentations at Microsoft AI and Gaming Research Summit 2021

MineRL papers

  • MineRL: A Large-Scale Dataset of Minecraft Demonstrations

  • The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors

  • Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

  • The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

  • Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020

2019 competitor code/papers

  • 1st place: paper.

  • 2nd place: paper, code.

  • 3rd place: paper, code.

  • 4th place: code.

  • 5th place: paper, code.

2020 competitor code/papers

  • 1st place: paper.

  • 2nd place: code.

  • 3rd place: code.

Other papers that use the MineRL environment

  • PiCoEDL: Discovery and Learning of Minecraft Navigation Goals from Pixels and Coordinates (CVPR Embodied AI Workshop, 2021)

  • Universal Value Iteration Networks: When Spatially-Invariant Is Not Universal (AAAI, 2020)

  • Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft

  • Follow up paper from the #1 team in 2019 (obtains diamond): paper, code.

  • Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution (obtains diamond): paper, code.

Other

  • Data analysis for vector obfuscation/kmeans

  • Malmo and MineRL tutorial

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© Copyright 2020, William H. Guss, Brandon Houghton. Revision ccc8c659.

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