Installation

============

.. _installation:

MyoSuite uses git submodules to resolve dependencies.

Please follow steps exactly as below to install correctly.

Requirements

~~~~~~~~~~~~

* python >= 3.8 (if needed follow instructions `here <https://docs.conda.io/en/latest/miniconda.html>`_ for installing python and conda)

* mujoco >= 2.3.6

Installing the pip package

~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: bash

   conda create --name MyoSuite python=3.8

   conda activate MyoSuite

   pip install -U myosuite

(alternative) Installing from source

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To get started with MyoSuite, clone this repo with pre-populated submodule dependencies

.. code-block:: bash

   git clone --recursive https://github.com/facebookresearch/myosuite.git

   cd myosuite

   pip install -e .

Testing the installation

~~~~~~~~~~~~~~~~~~~~~~~~

You can test the installation using

.. code-block:: bash

   python -m myosuite.tests.test_myo

You can visualize the environments with random controls using the below command

.. code-block:: bash

   python -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0

.. note::

   On MacOS, the need of a launch_passive option might require that the Python script be run under `mjpython` i.e. 

   `mjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0`

Examples

~~~~~~~~~

It is possible to create and interface with MyoSuite environments like any other OpenAI gym environments.

For example, to use the ``myoElbowPose1D6MRandom-v0`` environment it is possible simply to run:

.. code-block:: python

   from myosuite.utils import gym

   env = gym.make('myoElbowPose1D6MRandom-v0')

   env.reset()

   for _ in range(1000):

     env.mj_render()

     env.step(env.action_space.sample()) # take a random action

   env.close()

By default it will activate the simulator and the following visualization is expected:

.. image:: images/test_env.png

  :width: 300