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Task and example code for the Malmo Collaborative AI Challenge
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The Malmo Collaborative AI Challenge

This repository contains the task definition and example code for the Malmo Collaborative AI Challenge. This challenge is organized to encourage research in collaborative AI - to work towards AI agents that learn to collaborate to solve problems and achieve goals. You can find additional details, including terms and conditions, prizes and information on how to participate at the Challenge Homepage.

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Notes for challenge participants: Once you and your team decide to participate in the challenge, please make sure to register your team at our Registration Page. On the registration form, you need to provide a link to the GitHub repository that will contain your solution. We recommend that you fork this repository (learn how), and provide address of the forked repo. You can then update your submission as you make progress on the challenge task. We will consider the version of the code on branch master at the time of the submission deadline as your challenge submission. Your submission needs to contain code in working order, a 1-page description of your approach, and a 1-minute video that shows off your agent. Please see the challenge terms and conditions for further details.

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Minimal installation

pip install -e git+


git clone
cd malmo-challenge
pip install -e .

Optional extensions

Some of the example code uses additional dependencies to provide 'extra' functionality. These can be installed using:

pip install -e '.[extra1, extra2]'

For example to install gym and chainer:

pip install -e '.[gym]'

Or to install all extras:

pip install -e '.[all]'

The following extras are available:

  • gym: OpenAI Gym is an interface to a wide range of reinforcement learning environments. Installing this extra enables the Atari example agents in samples/atari to train on the gym environments. Note that OpenAI gym atari environments are currently not available on Windows.
  • tensorflow: TensorFlow is a popular deep learning framework developed by Google. In our examples it enables visualizations through TensorBoard.

Getting started

Play the challenge task

The challenge task takes the form of a mini game, called Pig Chase. Learn about the game, and try playing it yourself on our Pig Chase Challenge page.

Run your first experiment

See how to run your first baseline experiment on the Pig Chase Challenge page.

Next steps

Run an experiment in Docker on Azure

Docker is a virtualization platform that makes it easy to deploy software with all its dependencies. We use docker to run experiments locally or in the cloud. Details on how to run an example experiment using docker are in the docker README.

Compare your results against other teams:

We provide you a leaderboard website where you can compare your results against the other participants.


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