flatland-rl 2.2.0

Multi Agent Reinforcement Learning on Trains

🚂 Flatland

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

The base environment is a two-dimensional grid in which many agents can be placed, and each agent must solve one or more navigational tasks in the grid world.

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

🏆 Challenges

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

• NeurIPS 2020 Challenge
• 2019 Challegent

📦 Setup

Prerequisites

• Install Anaconda
• Create a new conda environment:

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

• Install the necessary dependencies:

$ conda install -c conda-forge cairosvg pycairo
$ conda install -c anaconda tk  

Stable Release

You can 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.

You can clone the public repository:

$ git clone [e-mail]:flatland/flatland.git

Once you have a copy of the source, you can 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, 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

• Discord Channel
• Discussion Forum
• Issue Tracker

🔗 Partners