Flatland
About Flatland
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.
This library was developed by SBB, AIcrowd and numerous contributors and AIcrowd research fellows from the AIcrowd community.
This library was developed specifically for the 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
Installation
Installation Prerequistes
$ 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
Install Flatland
Stable Release
To install flatland, run this command in your terminal:
$ pip install flatland-rl
This is the preferred method to install flatland, as it will always install the most recent stable release.
If you don't have pip
_ installed, this Python installation guide
_ can guide
you through the process.
.. _pip: https://pip.pypa.io .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
From sources
The sources for flatland can be downloaded from gitlab
You can clone the public repository:
$ git clone git@gitlab.aicrowd.com: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
Jupyter Canvas Widget
If you work with jupyter notebook you need to install the Jupyer Canvas Widget. To install the Jupyter Canvas Widget read also https://github.com/Who8MyLunch/Jupyter_Canvas_Widget#installation
Basic Usage
Basic usage of the RailEnv environment used by the Flatland Challenge
import numpy as np
import time
from flatland.envs.rail_generators import complex_rail_generator
from flatland.envs.schedule_generators import complex_schedule_generator
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
NUMBER_OF_AGENTS = 10
env = RailEnv(
width=20,
height=20,
rail_generator=complex_rail_generator(
nr_start_goal=10,
nr_extra=1,
min_dist=8,
max_dist=99999,
seed=0),
schedule_generator=complex_schedule_generator(),
number_of_agents=NUMBER_OF_AGENTS)
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
for step in range(100):
_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)
time.sleep(0.3)
and ideally you should see something along the lines of
Best of Luck !!
Communication
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.