This repository contains examples and small tasks on getting started with MineRL environment.
To begin, install the requirements for MineRL,
and then install Python requirements with with pip install -r requirements.txt
. We also have Colab notebooks
in case installing these libraries is not possible.
If you have any questions, you can reach us on Discord. If you spot typos/bugs in any of the tasks or this repo, do tell us via Github issues!
Stars indicate the difficulty of the task. Click the task to see more details.
⭐ Getting started with MineRL.
- Start by playing bit of Minecraft via MineRL with
playing_with_minerl.py
script. - Check out
getting_familiar_with_minerl_and_gym.py
and follow the instructions to get familiar with the agent-environment (Gym) API. - You can find the latter task on Colab here.
⭐⭐ Improve Intro baseline of the Diamond competition.
- Step-by-step instructions on how to improve a simple, fully-scripted agent for obtaining wood and stone in the MineRLObtainDiamond-v0 task.
- Start out by opening this document and following the instructions.
- If you are overwhelmingly stuck, you may look for reference answers from this documentation.
⭐⭐ Implementing behavioural cloning from (almost) scratch.
- Start by opening up
behavioural_cloning.py
and following the instructions at the beginning of the file. - You can also find the task on Colab here.
- You can find a crude reference answers in this Colab notebook. This task is built on the BC + scripted baseline solution.
⭐⭐⭐ Learn how to use stable-baselines and imitation libraries with MineRL.
- This walk-through demonstrates how to combine well-established reinforcement learning (stable-baselines3) and imitation learning (imitation) libraries with MineRL to train more sophisticated agents.
- Start by opening this Colab link.
⭐⭐⭐ Improve Research baseline of the Diamond competition.
- Similar to the second task here, but in a more difficult setting where you may not manually encode actions.
- Get started by opening this documentation.
- Once done, you can check reference answers from here.
⭐⭐⭐ Useful utilities for the BASALT 2021 competition.
- A collection and a walkthrough of approaches and methods that are useful for the BASALT competition (learning without rewards).
- Get started by opening this Colab.