Awesome

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:

Overview

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},
}

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.

• 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.

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.