Status: Maintenance (expect bug fixes and minor updates)

mujoco-py

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.

Synopsis

Requirements

The following platforms are currently supported:

Linux with Python 3.6. See the Dockerfile for 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 0.5 branch. The latest release there is 0.5.7 which can be installed with pip install mujoco-py==0.5.7.

Install MuJoCo

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 mujoco200 directory into ~/.mujoco/mujoco200, and place your license key (the mjkey.txt file from your email) at ~/.mujoco/mjkey.txt.

If you want to specify a nonstandard location for the key and package, use the env variables MUJOCO_PY_MJKEY_PATH and MUJOCO_PY_MUJOCO_PATH.

Install and use `mujoco-py`

To include mujoco-py in your own package, add it to your requirements like so:

mujoco-py<2.1,>=2.0

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.

Troubleshooting

Missing GLFW

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

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

Usage Examples

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 TextureModder can be used to randomize object textures
internal_functions.py: shows how to call raw mujoco functions like mjv_room2model
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.

Development

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.

Changelog

03/08/2018: We removed MjSimPool, because most of benefit one can get with multiple processes having single simulation.

Credits

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

Lamperougeyxy/mujoco-py