Status: Deprecated
New users should depend on the official MuJoCo Python bindings.
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 2.1 released on 2021-10-18.
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 2.0.2.0. One known good past version is 1.50.1.68.
- Python 2 has been DEPRECATED and removed in 1.50.1.0. Python 2 users can stay on the
0.5branch. The latest release there is0.5.7which can be installed withpip install mujoco-py==0.5.7.
- Download the MuJoCo version 2.1 binaries for Linux or OSX.
- Extract the downloaded
mujoco210directory into~/.mujoco/mujoco210.
If you want to specify a nonstandard location for the package,
use the env variable MUJOCO_PY_MUJOCO_PATH.
To include mujoco-py in your own package, add it to your requirements like so:
mujoco-py<2.2,>=2.1
To play with mujoco-py interactively, follow these steps:
$ pip3 install -U 'mujoco-py<2.2,>=2.1'
$ 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.
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:$PATH"
export CC="/usr/local/opt/llvm/bin/clang"
export CXX="/usr/local/opt/llvm/bin/clang++"
export CXX11="/usr/local/opt/llvm/bin/clang++"
export CXX14="/usr/local/opt/llvm/bin/clang++"
export CXX17="/usr/local/opt/llvm/bin/clang++"
export CXX1X="/usr/local/opt/llvm/bin/clang++"
export LDFLAGS="-L/usr/local/opt/llvm/lib"
export CPPFLAGS="-I/usr/local/opt/llvm/include"Note: Don't forget to source your .bashrc/.zshrc after editing it and try to install mujoco-py again:
# Make sure your python environment is activated
pip install -U 'mujoco-py<2.2,>=2.1'A common error when installing is:
raise ImportError("Failed to load GLFW3 shared library.")
Which happens when the glfw python package fails to find a GLFW dynamic library.
MuJoCo ships with its own copy of this library, which can be used during installation.
Add the path to the mujoco bin directory to your dynamic loader:
LD_LIBRARY_PATH=$HOME/.mujoco/mujoco210/bin pip install mujoco-py
This is particularly useful on Ubuntu 14.04, which does not have a GLFW package.
Because 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 bodiesdisco_fetch.py: shows howTextureModdercan be used to randomize object texturesinternal_functions.py: shows how to call raw mujoco functions likemjv_room2modelmarkers_demo.py: shows how to add visualization-only geoms to the viewerserialize_model.py: shows how to save and restore a modelsetting_state.py: shows how to reset the simulation to a given statetosser.py: shows a simple actuated object sorting robot application
See the full documentation for advanced usage.
To run the provided unit and integrations tests:
make test
To test GPU-backed rendering, run:
make test_gpu
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
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