Home AI News Introduction to dm_control: A Tutorial and Overview of the Software

Introduction to dm_control: A Tutorial and Overview of the Software


DM_Control Software Tutorial: Overview and Features

If you’re interested in learning more about the dm_control software and its features, there’s a helpful public colab notebook available here.

Introduction to DM_Control

DM_Control is a software library that offers a wide range of tools and functionalities for working with AI-controlled entities and environments. Its infrastructure and environments are designed to simplify the development of AI models and systems.


  • The MuJoCo Python wrapper is an autogenerated tool that provides complete access to the underlying engine. It allows for seamless integration with the dm_control software.
  • PyMJCF is a powerful Document Object Model library that represents MuJoCo model elements as a hierarchy of Python objects. It makes it easier to work with and modify complex models.
  • Composer is the high-level “game engine” of dm_control. It streamlines the composition of entities into scenes and allows for the definition of observations, rewards, terminations, and general game logic.
  • The Locomotion framework introduces abstract entities such as the Arena and Walker, which facilitate locomotion-like tasks in AI models.


  • The Control Suite includes various environments for testing and training AI models. This includes a new quadruped environment and a dog environment.
  • There are also several locomotion tasks, including soccer, that can be used to train AI models.
  • For those interested in robotics, dm_control offers single arm robotic manipulation tasks using snap-together bricks.


Named Indexing

One of the standout features of dm_control is its support for named indexing. Instead of using cumbersome numerical indexing, you can use meaningful names to access specific model elements. For example, instead of writing “fingertip_height = physics.data.geom_xpos[7, 2]”, you can write “fingertip_height = physics.named.data.geom_xpos[‘fingertip’, ‘z’]”. This makes the code more readable and less prone to errors.


The PyMJCF library is a Python object hierarchy that closely corresponds to a MuJoCo model. It introduces the “attach()” method, which allows for the attachment of models to one another. This is particularly useful for creating complex creatures and scenes, as demonstrated in the tutorial.


Composer is the core framework of dm_control. It defines the order of runtime function calls and abstracts important game elements such as rewards, terminations, and observations. It also offers useful submodules like composer.Observable, which adds noise and delays to sensors, and composer.Variation, which allows for randomization of simulation quantities.


The Locomotion framework in dm_control introduces two key entities: the Walker and the Arena. The Walker is a controllable entity with locomotion-related methods, while the Arena is a dynamic scene where the walker can perform various tasks. Creating a humanoid walker and an arena with a task can be done with just four function calls.

New Control Suite Domains


The dm_control software includes a generic quadruped domain with various locomotion tasks such as walking and running. It also offers challenging tasks like rough terrain navigation and ball dribbling.


The dog domain in dm_control is based on a detailed skeleton model and includes a task that requires precision grasping with the mouth. It’s a challenging environment for AI models to master.


DeepMind has put together a fast-paced montage showcasing various tasks and environments available in dm_control. It’s a great way to see the software in action.

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