dm_control: a physics-based simulation stack for Reinforcement Learning and continuous control using MuJoCo

What it solves

It provides a standardized software stack for physics-based simulation and Reinforcement Learning (RL) environments, specifically designed for continuous control tasks.

How it works

The project uses the MuJoCo physics engine as its foundation. It provides Python bindings to the engine, a set of pre-built RL environments (the suite), and an interactive viewer for visualizing simulations. For more complex tasks, it includes libraries for composing MuJoCo models (MJCF) and defining environments from reusable components (composer).

Who it’s for

Researchers and developers working on Reinforcement Learning, robotics simulation, and continuous control of physical systems.

Highlights

• MuJoCo Integration: Deeply integrated with the MuJoCo physics engine for high-fidelity simulation.
• Comprehensive Tooling: Includes a suite of RL environments, a model composer, and an interactive viewer.
• Flexible Rendering: Supports multiple OpenGL rendering backends (EGL, GLFW, and OSMesa) for both headless and windowed environments.
• Specialized Tasks: Includes additional libraries for custom locomotion and multi-agent soccer tasks.