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Multi Agent Reinforcement Learning on Trains

Project description

🚂 Flatland

Flatland

Flatland is a open-source toolkit for developing and comparing Multi Agent Reinforcement Learning algorithms in little (or ridiculously large!) gridworlds.

The official documentation contains full details about the environment and problem statement

Flatland is tested with Python 3.6 and 3.7 on modern versions of macOS, Linux and Windows. You may encounter problems with graphical rendering if you use WSL. Your contribution is welcome if you can help with this!

🏆 Challenges

This library was developed specifically for the AIcrowd Flatland challenges in which we strongly encourage you to take part in!

📦 Setup

Prerequisites (optional)

Install Anaconda and create a new conda environment:

$ conda create python=3.6 --name flatland-rl
$ conda activate flatland-rl

Stable release

Install Flatland from pip:

$ pip install flatland-rl

This is the preferred method to install Flatland, as it will always install the most recent stable release.

From sources

The Flatland code source is available from AIcrowd gitlab.

Clone the public repository:

$ git clone git@gitlab.aicrowd.com:flatland/flatland.git

Once you have a copy of the source, install it with:

$ python setup.py install

Test installation

Test that the installation works:

$ flatland-demo

You can also run the full test suite:

python setup.py test

👥 Credits

This library was developed by SBB, Deutsche Bahn, AIcrowd and numerous contributors and AIcrowd research fellows from the AIcrowd community.

➕ Contributions

Please follow the Contribution Guidelines for more details on how you can successfully contribute to the project. We enthusiastically look forward to your contributions!

💬 Communication

🔗 Partners

Project details


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