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dm_control: Google DeepMind Infrastructure for Physics-Based Simulation.

Google 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: Open In Colab

Overview

This package consists of the following "core" components:

Additionally, the following components are available for the creation of more complex control 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},
}

Installation

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 the mujoco 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 install dm_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.

Versioning

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/google-deepmind/dm_control.git.

Rendering

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.

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.

Additional instructions for Homebrew users on macOS

  1. 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).

  2. Before running, the DYLD_LIBRARY_PATH environment variable needs to be updated with the path to the GLFW library. This can be done by running export DYLD_LIBRARY_PATH=$(brew --prefix)/lib:$DYLD_LIBRARY_PATH.