Metadata-Version: 2.1
Name: flatland-rl
Version: 2.1.9
Summary: Multi Agent Reinforcement Learning on Trains
Home-page: https://gitlab.aicrowd.com/flatland/flatland
Author: S.P. Mohanty
Author-email: mohanty@aicrowd.com
License: UNKNOWN
Description: Flatland
        ========
        
        ![Test Running](https://gitlab.aicrowd.com/flatland/flatland/badges/master/pipeline.svg)![Test Coverage](https://gitlab.aicrowd.com/flatland/flatland/badges/master/coverage.svg "asdff")
        
        
        ![Flatland](https://i.imgur.com/0rnbSLY.gif)
        
        ## 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](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/)
        
        
        ## Installation
        ### Installation Prerequistes
        
        * Install [Anaconda](https://www.anaconda.com/distribution/) by following the instructions [here](https://www.anaconda.com/distribution/).
        * Create a new conda environment:
        
        ```console
        $ conda create python=3.6 --name flatland-rl
        $ conda activate flatland-rl
        ```
        
        * Install the necessary dependencies
        
        ```console
        $ 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:
        
        ```console
        $ 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](https://gitlab.aicrowd.com/flatland/flatland)
        
        You can clone the public repository:
        ```console
        $ git clone git@gitlab.aicrowd.com:flatland/flatland.git
        ```
        
        Once you have a copy of the source, you can install it with:
        
        ```console
        $ python setup.py install
        ```
        
        ### Test installation
        
        Test that the installation works
        
        ```console
        $ 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]([https://github.com/Who8MyLunch/Jupyter_Canvas_Widget#installation)
        
        ## Basic Usage
        
        Basic usage of the RailEnv environment used by the Flatland Challenge (also see [Example](https://gitlab.aicrowd.com/flatland/flatland/blob/master/examples/introduction_flatland_2_1.py))
        
        
        ```python
        from flatland.envs.observations import GlobalObsForRailEnv
        # First of all we import the Flatland rail environment
        from flatland.envs.rail_env import RailEnv
        from flatland.envs.rail_generators import sparse_rail_generator
        from flatland.envs.schedule_generators import sparse_schedule_generator
        # We also include a renderer because we want to visualize what is going on in the environment
        from flatland.utils.rendertools import RenderTool, AgentRenderVariant
        
        width = 100  # With of map
        height = 100  # Height of map
        nr_trains = 50  # Number of trains that have an assigned task in the env
        cities_in_map = 20  # Number of cities where agents can start or end
        seed = 14  # Random seed
        grid_distribution_of_cities = False  # Type of city distribution, if False cities are randomly placed
        max_rails_between_cities = 2  # Max number of tracks allowed between cities. This is number of entry point to a city
        max_rail_in_cities = 6  # Max number of parallel tracks within a city, representing a realistic trainstation
        
        rail_generator = sparse_rail_generator(max_num_cities=cities_in_map,
                                               seed=seed,
                                               grid_mode=grid_distribution_of_cities,
                                               max_rails_between_cities=max_rails_between_cities,
                                               max_rails_in_city=max_rail_in_cities,
                                               )
        
        # The schedule generator can make very basic schedules with a start point, end point and a speed profile for each agent.
        # The speed profiles can be adjusted directly as well as shown later on. We start by introducing a statistical
        # distribution of speed profiles
        
        # Different agent types (trains) with different speeds.
        speed_ration_map = {1.: 0.25,  # Fast passenger train
                            1. / 2.: 0.25,  # Fast freight train
                            1. / 3.: 0.25,  # Slow commuter train
                            1. / 4.: 0.25}  # Slow freight train
        
        # We can now initiate the schedule generator with the given speed profiles
        
        schedule_generator = sparse_schedule_generator(speed_ration_map)
        
        # We can furthermore pass stochastic data to the RailEnv constructor which will allow for stochastic malfunctions
        # during an episode.
        
        stochastic_data = {'prop_malfunction': 0.3,  # Percentage of defective agents
                           'malfunction_rate': 30,  # Rate of malfunction occurence
                           'min_duration': 3,  # Minimal duration of malfunction
                           'max_duration': 20  # Max duration of malfunction
                           }
        
        # Custom observation builder without predictor
        observation_builder = GlobalObsForRailEnv()
        
        # Custom observation builder with predictor, uncomment line below if you want to try this one
        # observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
        
        # Construct the enviornment with the given observation, generataors, predictors, and stochastic data
        env = RailEnv(width=width,
                      height=height,
                      rail_generator=rail_generator,
                      schedule_generator=schedule_generator,
                      number_of_agents=nr_trains,
                      malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
                      obs_builder_object=observation_builder,
                      remove_agents_at_target=True  # Removes agents at the end of their journey to make space for others
                      )
        
        # Initiate the renderer
        env_renderer = RenderTool(env, gl="PILSVG",
                                  agent_render_variant=AgentRenderVariant.AGENT_SHOWS_OPTIONS_AND_BOX,
                                  show_debug=False,
                                  screen_height=1080,  # Adjust these parameters to fit your resolution
                                  screen_width=1920)  # Adjust these parameters to fit your resolution
        
        
        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, info = 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
        
        ![Flatland](https://i.imgur.com/Pc9aH4P.gif)
        
        Best of Luck !!
        
        ## Communication
        * [Official Documentation](http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/)
        * [Discussion Forum](https://discourse.aicrowd.com/c/flatland-challenge)
        * [Issue Tracker](https://gitlab.aicrowd.com/flatland/flatland/issues/)
        
        
        ## Contributions
        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
        <a href="https://sbb.ch" target="_blank"><img src="https://i.imgur.com/OSCXtde.png" alt="SBB"/></a>
        <a href="https://www.aicrowd.com"  target="_blank"><img src="https://avatars1.githubusercontent.com/u/44522764?s=200&v=4" alt="AICROWD"/></a>
        
        
        
        
Keywords: flatland
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
