DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics.
An introductory tutorial for this package is available as a Colaboratory
notebook:
This package consists of the following "core" components:
-
dm_control.mujoco
: Libraries that provide Python bindings to the MuJoCo physics engine. -
dm_control.suite
: A set of Python Reinforcement Learning environments powered by the MuJoCo physics engine. -
dm_control.viewer
: An interactive environment viewer.
Additionally, the following components are available for the creation of more complex control tasks:
-
dm_control.mjcf
: A library for composing and modifying MuJoCo MJCF models in Python. -
dm_control.composer
: A library for defining rich RL environments from reusable, self-contained components. -
dm_control.locomotion
: Additional libraries for custom tasks. -
dm_control.locomotion.soccer
: Multi-agent soccer tasks.
If you use this package, please cite our accompanying publication:
@article{tunyasuvunakool2020,
title = {dm_control: Software and tasks for continuous control},
journal = {Software Impacts},
volume = {6},
pages = {100022},
year = {2020},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2020.100022},
url = {https://www.sciencedirect.com/science/article/pii/S2665963820300099},
author = {Saran Tunyasuvunakool and Alistair Muldal and Yotam Doron and
Siqi Liu and Steven Bohez and Josh Merel and Tom Erez and
Timothy Lillicrap and Nicolas Heess and Yuval Tassa},
}
Install dm_control
from PyPI by running
pip install dm_control
Note:
dm_control
cannot be installed in "editable" mode (i.e.pip install -e
).While
dm_control
has been largely updated to use the pybind11-based bindings provided via themujoco
package, at this time it still relies on some legacy components that are automatically generated from MuJoCo header files in a way that is incompatible with editable mode. Attempting to installdm_control
in editable mode will result in import errors like:ImportError: cannot import name 'constants' from partially initialized module 'dm_control.mujoco.wrapper.mjbindings' ...
The solution is to
pip uninstall dm_control
and then reinstall it without the-e
flag.
Starting from version 1.0.0, we adopt semantic versioning.
Prior to version 1.0.0, the dm_control
Python package was versioned 0.0.N
,
where N
was an internal revision number that increased by an arbitrary amount
at every single Git commit.
If you want to install an unreleased version of dm_control
directly from our
repository, you can do so by running pip install git+https://github.com/deepmind/dm_control.git
.
The MuJoCo Python bindings support three different OpenGL rendering backends:
EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and
OSMesa (purely software-based). At least one of these three backends must be
available in order render through dm_control
.
-
Hardware rendering with a windowing system is supported via GLFW and GLEW. On Linux these can be installed using your distribution's package manager. For example, on Debian and Ubuntu, this can be done by running
sudo apt-get install libglfw3 libglew2.0
. Please note that:dm_control.viewer
can only be used with GLFW.- GLFW will not work on headless machines.
-
"Headless" hardware rendering (i.e. without a windowing system such as X11) requires EXT_platform_device support in the EGL driver. Recent Nvidia drivers support this. You will also need GLEW. On Debian and Ubuntu, this can be installed via
sudo apt-get install libglew2.0
. -
Software rendering requires GLX and OSMesa. On Debian and Ubuntu these can be installed using
sudo apt-get install libgl1-mesa-glx libosmesa6
.
By default, dm_control
will attempt to use GLFW first, then EGL, then OSMesa.
You can also specify a particular backend to use by setting the MUJOCO_GL=
environment variable to "glfw"
, "egl"
, or "osmesa"
, respectively. When
rendering with EGL, you can also specify which GPU to use for rendering by
setting the environment variable MUJOCO_EGL_DEVICE_ID=
to the target GPU ID.
-
The above instructions using
pip
should work, provided that you use a Python interpreter that is installed by Homebrew (rather than the system-default one). -
Before running, the
DYLD_LIBRARY_PATH
environment variable needs to be updated with the path to the GLFW library. This can be done by runningexport DYLD_LIBRARY_PATH=$(brew --prefix)/lib:$DYLD_LIBRARY_PATH
.