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+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
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