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.. image:: https://gitlab.aicrowd.com/flatland/flatland/badges/master/pipeline.svg
:target: https://gitlab.aicrowd.com/flatland/flatland/pipelines
:alt: Test Running
.. image:: https://gitlab.aicrowd.com/flatland/flatland/badges/master/coverage.svg
:target: https://gitlab.aicrowd.com/flatland/flatland/pipelines
Flatland is a opensource 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. More details about the environment and the problem statement can be found in the `official docs <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/>`_.
This library was developed by `SBB <https://www.sbb.ch/en/>`_ , `AIcrowd <https://www.aicrowd.com/>`_ and numerous contributors and AIcrowd research fellows from the AIcrowd community.
This library was developed specifically for the `Flatland Challenge <https://www.aicrowd.com/challenges/flatland-challenge>`_ in which we strongly encourage you to take part in.
**NOTE This document is best viewed in the official documentation site at** `Flatland-RL Docs <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/readme.html>`_
Contents
===========
* `Official Documentation <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/readme.html>`_
* `About Flatland <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/about_flatland.html>`_
* `Installation <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/installation.html>`_
* `Getting Started <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/gettingstarted.html>`_
* `Frequently Asked Questions <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/FAQ.html>`_
* `Code Docs <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/modules.html>`_
* `Contributing Guidelines <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/contributing.html>`_
* `Discussion Forum <https://discourse.aicrowd.com/c/flatland-challenge>`_
* `Issue Tracker <https://gitlab.aicrowd.com/flatland/flatland/issues/>`_
* Install `Anaconda <https://www.anaconda.com/distribution/>`_ by following the instructions `here <https://www.anaconda.com/distribution/>`_
* Install the dependencies and the library
$ conda create python=3.6 --name flatland-rl
$ conda activate flatland-rl
$ conda install -c conda-forge cairosvg pycairo
$ conda install -c anaconda tk
$ pip install flatland-rl
Basic usage of the RailEnv environment used by the Flatland Challenge
.. code-block:: python
import numpy as np
import time
from flatland.envs.generators import complex_rail_generator
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
rail_generator=complex_rail_generator(
nr_start_goal=10,
nr_extra=1,
min_dist=8,
max_dist=99999,
seed=0),
env_renderer = RenderTool(env)
def my_controller():
"""
You are supposed to write this controller
"""
_action = {}
for _idx in range(NUMBER_OF_AGENTS):
_action[_idx] = np.random.randint(0, 5)
return _action
_action = my_controller()
obs, all_rewards, done, _ = env.step(_action)
print("Rewards: {}, [done={}]".format( all_rewards, done))
env_renderer.render_env(show=True, frames=False, show_observations=False)
and **ideally** you should see something along the lines of
:align: center
:width: 600px
Best of Luck !!
Contributions
=============
Flatland is an opensource project, and we very much value all and any contributions you make towards the project.
Please follow the `Contribution Guidelines <http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/contributing.html>`_ for more details on how you can successfully contribute to the project. We enthusiastically look forward to your contributions.
Partners
============
:target: https://sbb.ch
.. image:: https://avatars1.githubusercontent.com/u/44522764?s=200&v=4
:target: https://www.aicrowd.com
* Christian Eichenberger <christian.markus.eichenberger@sbb.ch>
* Adrian Egli <adrian.egli@sbb.ch>
* Mattias Ljungström
* Guillaume Mollard <guillaume.mollard2@gmail.com>
* Erik Nygren <erik.nygren@sbb.ch>
Acknowledgements
====================
* Vaibhav Agrawal <theinfamouswayne@gmail.com>