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image:: https://github.com/MushroomRL/mushroom-rl/actions/workflows/continuous_integration.yml/badge.svg?branch=dev\n   :target: https://github.com/MushroomRL/mushroom-rl/actions/workflows/continuous_integration.yml\n   :alt: Continuous Integration\n\n.. image:: https://readthedocs.org/projects/mushroomrl/badge/?version=latest\n   :target: https://mushroomrl.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n.. image:: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl/maintainability.svg\n   :target: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl\n   :alt: Maintainability\n\n.. image:: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl/coverage.svg\n   :target: https://qlty.sh/gh/MushroomRL/projects/mushroom-rl\n   :alt: Test Coverage\n\n**MushroomRL: Reinforcement Learning Python library.**\n\n.. contents:: **Contents of this document:**\n   :depth: 2\n\nWhat is MushroomRL\n==================\nMushroomRL is a Python Reinforcement Learning (RL) library whose modularity allows\nto easily use well-known Python libraries for tensor computation (e.g. PyTorch,\nTensorflow) and RL benchmarks (e.g. Gymnasium, PyBullet, Deepmind Control Suite).\nIt allows to perform RL experiments in a simple way providing classical RL algorithms\n(e.g. Q-Learning, SARSA, FQI), and deep RL algorithms (e.g. DQN, DDPG, SAC, TD3,\nTRPO, PPO).\n\n`Full documentation and tutorials available here \u003chttp://mushroomrl.readthedocs.io/en/latest/\u003e`_.\n\nInstallation\n============\n\nYou can do a minimal installation of ``MushroomRL`` with:\n\n.. code:: shell\n\n    pip3 install mushroom_rl\n\nInstalling everything\n---------------------\n``MushroomRL`` contains also some optional components e.g., support for ``Gymnasium``\nenvironments, Atari 2600 games from the ``Arcade Learning Environment``, and the support\nfor physics simulators such as ``Pybullet`` and ``MuJoCo``. \nSupport for these classes is not enabled by default.\n\nTo install the whole set of features, you will need additional packages installed.\nYou can install everything by running:\n\n.. code:: shell\n\n    pip3 install mushroom_rl[all]\n\nThis will install every dependency of MushroomRL, except the Plots dependency.\nFor ubuntu\u003e20.04, you may need to install pygame and gym dependencies:\n\n.. code:: shell\n\n    sudo apt -y install libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \\\n                     libsdl1.2-dev libsmpeg-dev libportmidi-dev ffmpeg libswscale-dev \\\n                     libavformat-dev libavcodec-dev swig\n\nNotice that you still need to install some of these dependencies for different operating systems, e.g. swig for macOS \n\nBelow is the code that you need to run to install the Plots dependencies:\n\n.. code:: shell\n\n    sudo apt -y install python3-pyqt5\n    pip3 install mushroom_rl[plots]\n\nYou might need to install external dependencies first. For more information about mujoco-py\ninstallation follow the instructions on the `project page \u003chttps://github.com/openai/mujoco-py\u003e`_\n\n    WARNING! when using conda, there may be issues with QT. You can fix them by adding the following lines to the code, replacing ``\u003cconda_base_path\u003e`` with the path to your conda distribution and ``\u003cenv_name\u003e`` with the name of the conda environment you are using:\n   \n.. code:: python\n\n   import os\n   os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = '\u003cconda_base_path\u003e/envs/\u003cenv_name\u003e/bin/platforms'\n\nTo use dm_control MushroomRL interface, install ``dm_control`` following the instruction that can\nbe found `here \u003chttps://github.com/deepmind/dm_control\u003e`_\n\nUsing Habitat and iGibson with MushroomRL\n-----------------------------------------\n\n`Habitat \u003chttps://aihabitat.org/\u003e`__ and `iGibson \u003chttp://svl.stanford.edu/igibson/\u003e`__\nare simulation platforms providing realistic and sensory-rich learning environments.\nIn MushroomRL, the agent's default observations are RGB images, but RGBD,\nagent sensory data, and other information can also be used.\n\n    If you have previous versions of iGibson or Habitat already installed, we recommend to remove them and do clean installs.\n\niGibson Installation\n^^^^^^^^^^^^^^^^^^^^\nFollow the `official guide \u003chttp://svl.stanford.edu/igibson/#install_env\u003e`__ and install its\n`assets \u003chttp://svl.stanford.edu/igibson/docs/assets.html\u003e`__ and\n`datasets \u003chttp://svl.stanford.edu/igibson/docs/dataset.html\u003e`__.\n\nFor ``\u003cMUSHROOM_RL PATH\u003e/mushroom-rl/examples/igibson_dqn.py`` you need to run\n\n.. code:: shell\n\n    python -m igibson.utils.assets_utils --download_assets\n    python -m igibson.utils.assets_utils --download_demo_data\n    python -m igibson.utils.assets_utils --download_ig_dataset\n\nYou can also use `third party datasets \u003chttps://github.com/StanfordVL/iGibson/tree/master/igibson/utils/data_utils/ext_scene\u003e`__.\n\nThe scene details are defined in a YAML file, that needs to be passed to the agent.\nSee ``\u003cIGIBSON PATH\u003e/igibson/test/test_house.YAML`` for an example.\n\n\nHabitat Installation\n^^^^^^^^^^^^^^^^^^^^\nFollow the `official guide \u003chttps://github.com/facebookresearch/habitat-lab/#installation\u003e`__\nand do a **full install** with `habitat_baselines`.\nThen you can download interactive datasets following\n`this \u003chttps://github.com/facebookresearch/habitat-lab#data\u003e`__ and\n`this \u003chttps://github.com/facebookresearch/habitat-lab#task-datasets\u003e`__.\nIf you need to download other datasets, you can use\n`this utility \u003chttps://github.com/facebookresearch/habitat-sim/blob/master/habitat_sim/utils/datasets_download.py\u003e`__.\n\nBasic Usage of Habitat\n^^^^^^^^^^^^^^^^^^^^^^\nWhen you create a ``Habitat`` environment, you need to pass a wrapper name and two\nYAML files: ``Habitat(wrapper, config_file, base_config_file)``.\n\n* The wrapper has to be among the ones defined in ``\u003cMUSHROOM_RL PATH\u003e/mushroom-rl/environments/habitat_env.py``,\n  and takes care of converting actions and observations in a gym-like format. If your task / robot requires it,\n  you may need to define new wrappers.\n\n* The YAML files define every detail: the Habitat environment, the scene, the\n  sensors available to the robot, the rewards, the action discretization, and any\n  additional information you may need. The second YAML file is optional, and\n  overwrites whatever was already defined in the first YAML.\n\n    If you use YAMLs from ``habitat-lab``, check if they define a YAML for\n    ``BASE_TASK_CONFIG_PATH``. If they do, you need to pass it as ``base_config_file`` to\n    ``Habitat()``. ``habitat-lab`` YAMLs, in fact, use relative paths, and calling them\n    from outside its root folder will cause errors.\n\n* If you use a dataset, be sure that the path defined in the YAML file is correct,\n  especially if you use relative paths. ``habitat-lab`` YAMLs use relative paths, so\n  be careful with that. By default, the path defined in the YAML file will be\n  relative to where you launched the python code. If your `data` folder is\n  somewhere else, you may also create a symbolic link.\n\nRearrange Task Example\n^^^^^^^^^^^^^^^^^^^^^^\n* Download the ReplicaCAD datasets (``--data-path data`` downloads them in the folder\n  from where you are launching your code)\n\n.. code:: shell\n\n    python -m habitat_sim.utils.datasets_download --uids replica_cad_dataset --data-path data\n\n* For this task we use ``\u003cHABITAT_LAB PATH\u003e/habitat_baselines/config/rearrange/rl_pick.yaml``.\n  This YAML defines ``BASE_TASK_CONFIG_PATH: configs/tasks/rearrange/pick.yaml``,\n  and since this is a relative path we need to overwrite it by passing its absolute path\n  as ``base_config_file`` argument to ``Habitat()``.\n\n* Then, ``pick.yaml`` defines the dataset to be used with respect to ``\u003cHABITAT_LAB PATH\u003e``.\n  If you have not used ``--data-path`` argument with the previous download command,\n  the ReplicaCAD datasets is now in ``\u003cHABITAT_LAB PATH\u003e/data`` and you need to\n  make a link to it\n\n.. code:: shell\n\n    ln -s \u003cHABITAT_LAB PATH\u003e/data/ \u003cMUSHROOM_RL PATH\u003e/mushroom-rl/examples/habitat\n\n* Finally, you can launch ``python habitat_rearrange_sac.py``.\n\nNavigation Task Example\n^^^^^^^^^^^^^^^^^^^^^^^\n* Download and extract Replica scenes\n\n    WARNING! The dataset is very large!\n\n.. code:: shell\n\n    sudo apt-get install pigz\n    git clone https://github.com/facebookresearch/Replica-Dataset.git\n    cd Replica-Dataset\n    ./download.sh replica-path\n\n* For this task we only use the custom YAML file ``pointnav_apartment-0.yaml``.\n\n* ``DATA_PATH: \"replica_{split}_apartment-0.json.gz\"`` defines the JSON file with\n  some scene details, such as the agent's initial position and orientation.\n  The ``{split}`` value is defined in the ``SPLIT`` key.\n\n    If you want to try new positions, you can sample some from the set of the scene's navigable points.\n    After initializing a ``habitat`` environment, for example ``mdp = Habitat(...)``,\n    run ``mdp.env._env._sim.sample_navigable_point()``.\n\n* ``SCENES_DIR: \"Replica-Dataset/replica-path/apartment_0\"`` defines the scene.\n  As said before, this path is relative to where you launch the script, thus we need to link the Replica folder.\n  If you launch ``habitat_nav_dqn.py`` from its example folder, run\n\n.. code:: shell\n\n    ln -s \u003cPATH TO\u003e/Replica-Dataset/ \u003cMUSHROOM_RL PATH\u003e/mushroom-rl/examples/habitat\n\n* Finally, you can launch ``python habitat_nav_dqn.py``.\n\n\n\nEditable Installation\n---------------------\n\nYou can also perform a local editable installation by using:\n\n.. code:: shell\n\n    pip install --no-use-pep517 -e .\n\nTo install also optional dependencies:\n\n.. code:: shell\n\n    pip install --no-use-pep517 -e .[all]\n\n\n\nHow to set and run and experiment\n=================================\nTo run experiments, MushroomRL requires a script file that provides the necessary information\nfor the experiment. Follow the scripts in the \"examples\" folder to have an idea\nof how an experiment can be run.\n\nFor instance, to run a quick experiment with one of the provided example scripts, run:\n\n.. code:: shell\n\n    python3 examples/car_on_hill_fqi.py\n\nCite MushroomRL\n===============\nIf you are using MushroomRL for your scientific publications, please cite:\n\n.. code:: bibtex\n\n    @article{JMLR:v22:18-056,\n        author  = {Carlo D'Eramo and Davide Tateo and Andrea Bonarini and Marcello Restelli and Jan Peters},\n        title   = {MushroomRL: Simplifying Reinforcement Learning Research},\n        journal = {Journal of Machine Learning Research},\n        year    = {2021},\n        volume  = {22},\n        number  = {131},\n        pages   = {1-5},\n        url     = {http://jmlr.org/papers/v22/18-056.html}\n    }\n\nHow to contact us\n=================\nFor any question, drop an e-mail at mushroom4rl@gmail.com.\n\nFollow us on Twitter `@Mushroom_RL \u003chttps://twitter.com/mushroom_rl\u003e`_!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmushroomrl%2Fmushroom-rl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmushroomrl%2Fmushroom-rl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmushroomrl%2Fmushroom-rl/lists"}