dm_control

Introduction to `dm_control`

dm_control is a comprehensive package developed by Google DeepMind, designed for physics-based simulation and reinforcement learning environments, particularly utilizing the MuJoCo physics engine. This toolkit is vital for developers and researchers interested in creating, simulating, and testing reinforcement learning models in dynamic environments.

Core Components

The dm_control package is structured around several core components:

dm_control.mujoco: This library provides Python bindings to the MuJoCo physics engine, which enables high-performance simulations of dynamic systems.
dm_control.suite: It offers a collection of reinforcement learning environments powered by MuJoCo, allowing users to experiment with and refine their RL algorithms.
dm_control.viewer: An interactive viewer that enhances the visualization of simulation environments, making it easier to observe and debug simulations.

Additionally, dm_control includes several advanced components for more complex tasks:

dm_control.mjcf: This library allows users to compose and modify MuJoCo MJCF models directly in Python, facilitating the creation of custom simulation models.
dm_control.composer: A tool for defining advanced RL environments using reusable components, streamlining the development process of rich and complex environmental interactions.
dm_control.locomotion and dm_control.locomotion.soccer: These libraries support custom tasks, including multi-agent soccer simulations, providing a platform for studying group dynamics and strategies.

Installation

dm_control is available on PyPI and can be installed using the following command:

pip install dm_control

Installation must be performed in a standard mode without using editable mode (-e), as some legacy components of dm_control are incompatible with this setup. If errors occur during an editable installation attempt, users should uninstall and reinstall the package without the editable flag.

Versioning

The package adheres to semantic versioning starting from version 1.0.0. Before reaching this milestone, version numbers were assigned using an internal numbering system that incremented with each Git commit. Users can also access unreleased versions directly from the repository to benefit from the latest updates.

Rendering Options

dm_control supports rendering via three different OpenGL backends:

EGL: A headless, hardware-accelerated backend suitable for machines without a display environment.
GLFW: This backend provides windowed, hardware-accelerated rendering, requiring a graphical desktop environment.
OSMesa: A software-based option that is purely rendering based, helpful when hardware acceleration is unavailable.

By default, the system prioritizes GLFW, then EGL, and lastly OSMesa. Users can set specific rendering backends via the environment variable MUJOCO_GL, and GPU preferences for EGL can be adjusted with MUJOCO_EGL_DEVICE_ID.

Special Considerations for macOS Users

For those on macOS using Homebrew, ensure Python is installed via Homebrew and update the DYLD_LIBRARY_PATH with the path to the GLFW library, which can be set via:

export DYLD_LIBRARY_PATH=$(brew --prefix)/lib:$DYLD_LIBRARY_PATH