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| It is frequently used with the associated Python module <code>mujoco_py</code> as an environment for reinforcement learning (RL) research. | | It is frequently used with the associated Python bindings <code>mujoco</code> as an environment for reinforcement learning (RL) research. |
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| | The module contains MuJoCo C/C++ library and its Python bindings. |
| __TOC__
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| ==Installation== <!--T:6--> | | == Library == |
| | In order to access headers and binaries, load the module: |
| | {{Command|module load mujoco}} |
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| === Base files === <!--T:2--> | | == Python bindings == |
| | To discover which are the compatible Python versions, run |
| | {{Command|module spider mujoco/2.2.2}} |
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| <!--T:7-->
| | 1. Load the required modules. |
| First, the .mujoco folder in your home directory must be set up. MuJoCo '''requires a licence key''' to be placed in
| | {{Command|module load mujoco python}} |
| ~/.mujoco/mjkey.txt. This key is obtained by e-mail after registration.
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| | 2. Import MuJoCo. |
| The appropriate (Linux) MuJoCo distribution must also be downloaded and unzipped into the ~/.mujoco directory from the ''Download'' section of the [https://www.roboti.us/index.html MuJoCo website].
| | {{Command|python -c "import mujoco"}} |
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| | If the command displays nothing, the import was successful. |
| Your ~/.mujoco folder should now look like this (the version might differ):
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| {{Command
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| |ls ~/.mujoco
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| |result=
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| mjkey.txt mjproj150
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| }}
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| === mujoco_py === <!--T:3-->
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| The Python interface to MuJoCo is the <code>mujoco_py</code> module. Getting it to work requires some special steps because library paths need to be patched.
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| 1. After using <code>module</code> to load the desired Python version, you should set up a clean virtual environment:
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| <!--T:12-->
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| {{Commands
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| |mkdir mujoco_env
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| |virtualenv mujoco_env
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| |source mujoco_env/bin/activate
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| }}
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| 2. Install the <code>mujoco_py</code> moduleː
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| {{Command
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| |prompt=(mujoco_env) [name@server $]
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| |pip install mujoco_py
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| }}
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| <!--T:13-->
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| 3. Patch the paths with setrpaths.sh. Use the appropriate python version in the commands below if it is not 3.5:
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| <!--T:14-->
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| {{Commands
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| |prompt=(mujoco_env) [name@server $]
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| |setrpaths.sh --path $VIRTUAL_ENV/lib/python3.5/site-packages/mujoco_py
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| |setrpaths.sh --add_origin --path $HOME/.mujoco
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| |export LD_LIBRARY_PATH=$HOME/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH
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| }}
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| <!--T:15-->
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| 4. Run the following command:
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| | |
| <!--T:16-->
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| {{Command
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| |prompt=(mujoco_env) [name@server $]
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| |python -c "import mujoco_py"}}
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| <!--T:17-->
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| It should have crashed, but the necessary paths will have been created to run:
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| <!--T:18-->
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| {{Command
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| |prompt=(mujoco_env) [name@server $]
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| |setrpaths.sh --path $VIRTUAL_ENV/lib/python3.5/site-packages/mujoco_py}}
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| <!--T:19-->
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| After that last command, mujoco_py should be ready to use in the virtual environment. If everything is installed correctly, this next command should run without any errors:
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| {{Command
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| |prompt=(mujoco_env) [name@server $]
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| |python -c "import mujoco_py"}}
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| </translate> | | </translate> |
MuJoCo stands for Multi-Joint dynamics with Contact. It is a physics engine aiming to facilitate research and development in robotics, biomechanics, graphics and animation, and other areas where fast and accurate simulation is needed.[1]
It is frequently used with the associated Python bindings mujoco as an environment for reinforcement learning (RL) research.
The module contains MuJoCo C/C++ library and its Python bindings.
Library
In order to access headers and binaries, load the module:
[name@server ~]$ module load mujoco
Python bindings
To discover which are the compatible Python versions, run
[name@server ~]$ module spider mujoco/2.2.2
1. Load the required modules.
[name@server ~]$ module load mujoco python
2. Import MuJoCo.
[name@server ~]$ python -c "import mujoco"
If the command displays nothing, the import was successful.
