Status: Maintenance (expect bug fixes and minor updates)
MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts.
mujoco-py allows using MuJoCo from Python 3.
This library has been updated to be compatible with MuJoCo version 2.0 released on 10/1/2018.
The following platforms are currently supported:
- Linux with Python 3.6+. See the
Dockerfilefor the canonical list of system dependencies.
- OS X with Python 3.6+.
The following platforms are DEPRECATED and unsupported:
- Windows support has been DEPRECATED and removed in 220.127.116.11. One known good past version is 18.104.22.168.
- Python 2 has been DEPRECATED and removed in 22.214.171.124. Python 2 users can stay on the
0.5branch. The latest release there is
0.5.7which can be installed with
pip install mujoco-py==0.5.7.
- Obtain a 30-day free trial on the MuJoCo website or free license if you are a student. The license key will arrive in an email with your username and password.
- Download the MuJoCo version 2.0 binaries for Linux or OSX.
- Unzip the downloaded
~/.mujoco/mujoco200, and place your license key (the
mjkey.txtfile from your email) at
If you want to specify a nonstandard location for the key and package,
use the env variables
Install and use
mujoco-py in your own package, add it to your requirements like so:
To play with
mujoco-py interactively, follow these steps:
$ pip3 install -U 'mujoco-py<2.1,>=2.0' $ python3 import mujoco_py import os mj_path, _ = mujoco_py.utils.discover_mujoco() xml_path = os.path.join(mj_path, 'model', 'humanoid.xml') model = mujoco_py.load_model_from_path(xml_path) sim = mujoco_py.MjSim(model) print(sim.data.qpos) # [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] sim.step() print(sim.data.qpos) # [-2.09531783e-19 2.72130735e-05 6.14480786e-22 -3.45474715e-06 # 7.42993721e-06 -1.40711141e-04 -3.04253586e-04 -2.07559344e-04 # 8.50646247e-05 -3.45474715e-06 7.42993721e-06 -1.40711141e-04 # -3.04253586e-04 -2.07559344e-04 -8.50646247e-05 1.11317030e-04 # -7.03465386e-05 -2.22862221e-05 -1.11317030e-04 7.03465386e-05 # -2.22862221e-05]
See the full documentation for advanced usage.
You're on MacOS and you see
clang: error: unsupported option '-fopenmp'
If this happend during installation or just running
python -c "import mujoco_py" then the issue seems to be related to this and the TL;DR is that for macOS the default compiler Apple clang LLVM does not support openmp. So you can try to install another clang/llvm installation. For example (requires brew):
brew install llvm brew install boost brew install hdf5 # Add this to your .bashrc/.zshrc: export PATH="/usr/local/opt/llvm/bin:$PATH" export CC="/usr/local/opt/llvm/bin/clang" export CXX="/usr/local/opt/llvm/bin/clang++" export CXX11="/usr/local/opt/llvm/bin/clang++" export CXX14="/usr/local/opt/llvm/bin/clang++" export CXX17="/usr/local/opt/llvm/bin/clang++" export CXX1X="/usr/local/opt/llvm/bin/clang++" export LDFLAGS="-L/usr/local/opt/llvm/lib" export CPPFLAGS="-I/usr/local/opt/llvm/include"
Note: Don't forget to source your
.bashrc/.zshrc after editing it and try to install
# Make sure your python environment is activated pip install -U 'mujoco-py<2.1,>=2.0'
A common error when installing is:
raise ImportError("Failed to load GLFW3 shared library.")
Which happens when the
glfw python package fails to find a GLFW dynamic library.
MuJoCo ships with its own copy of this library, which can be used during installation.
Add the path to the mujoco bin directory to your dynamic loader:
LD_LIBRARY_PATH=$HOME/.mujoco/mujoco200/bin pip install mujoco-py
This is particularly useful on Ubuntu 14.04, which does not have a GLFW package.
Ubuntu installtion troubleshooting
mujoco_py has compiled native code that needs to be linked to a supplied MuJoCo binary, it's installation
on linux can be more challenging than pure Python source packages.
To install mujoco-py on Ubuntu, make sure you have the following libraries installed:
sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3
If you installed above libraries and you still see an error that
-lGL cannot be found, most likely you need
to create the symbolic link directly:
sudo ln -s /usr/lib/x86_64-linux-gnu/libGL.so.1 /usr/lib/x86_64-linux-gnu/libGL.so
A number of examples demonstrating some advanced features of
mujoco-py can be found in
examples/. These include:
body_interaction.py: shows interactions between colliding bodies
disco_fetch.py: shows how
TextureModdercan be used to randomize object textures
internal_functions.py: shows how to call raw mujoco functions like
markers_demo.py: shows how to add visualization-only geoms to the viewer
serialize_model.py: shows how to save and restore a model
setting_state.py: shows how to reset the simulation to a given state
tosser.py: shows a simple actuated object sorting robot application
See the full documentation for advanced usage.
To run the provided unit and integrations tests:
To test GPU-backed rendering, run:
This is somewhat dependent on internal OpenAI infrastructure at the moment, but it should run if you change the
Makefile parameters for your own setup.
- 03/08/2018: We removed MjSimPool, because most of benefit one can get with multiple processes having single simulation.
mujoco-py is maintained by the OpenAI Robotics team. Contributors include:
- Alex Ray
- Bob McGrew
- Jonas Schneider
- Jonathan Ho
- Peter Welinder
- Wojciech Zaremba
- Jerry Tworek