dm-control
Continuous control environments and MuJoCo Python bindings.
Decision gist · record as of 2026-08-14
Yes. dm_control is actively maintained, has no known vulnerabilities, installs with low friction, and is widely used in RL research. Install it if you need a physics simulation framework for continuous control research or prototyping. Be aware that editable mode installation is not supported and you must have an OpenGL backend available on your system.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires at least one OpenGL rendering backend (GLFW, EGL, or OSMesa) installed on the system; GLFW requires a windowing system and cannot be used on headless machines.
- On Linux: libglfw3, libglew2.0, or libgl1-mesa-glx + libosmesa6 depending on backend choice.
- Low friction install with a wheel distribution available.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for research and production applications.
last release 2026-07-28 (17 days) · last repo commit 2026-08-06 · 4,665 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 444,317 downloads/mo, #6,622 on PyPI
Alternatives
Verify before relying
pip install dm_control
import dm_control
from dm_control import suite
env = suite.load(domain_name='cartpole', task_name='balance')
action_spec = env.action_spec()
observation = env.reset()- Whether the 15 runtime dependencies (including mujoco, numpy, scipy, lxml, pyopengl, glfw) are all required for basic usage or if some are optional for specific components.
- Performance characteristics and scalability limits for large-scale multi-agent simulations beyond the soccer tasks mentioned.
- Compatibility with specific MuJoCo versions beyond what the mujoco package dependency specifies.
What it is and what it does
dm_control is Google DeepMind's infrastructure for physics-based simulation and reinforcement learning, built on the MuJoCo physics engine. It provides Python bindings to MuJoCo, a curated suite of RL environments (cartpole, humanoid, walker, etc.), an interactive viewer for debugging, and higher-level tools like MJCF model composition and a component-based environment builder for creating custom control tasks.
The package is designed for researchers and practitioners building continuous control agents. It handles the complexity of physics simulation, rendering, and environment management, letting you focus on algorithm development. The suite component offers standardized benchmarks; the viewer provides real-time visualization; and the composer and mjcf modules support building custom multi-agent scenarios like soccer tasks. It requires modern Python (3.9+) and system OpenGL libraries for rendering.
Use it for
- Benchmark reinforcement learning algorithms on standardized continuous control tasks like humanoid locomotion or manipulation.
- Develop and test custom physics-based control environments by composing reusable components with the composer library.
- Visualize and debug agent behavior in real-time using the interactive viewer during development.
- Build multi-agent scenarios such as competitive or cooperative tasks using the soccer module and environment composition tools.
- Prototype robot control policies in simulation before deployment, using MuJoCo's accurate physics and efficient computation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dm_control is actively maintained, has no known vulnerabilities, installs with low friction, and is widely used in RL research. Install it if you need a physics simulation framework for continuous control research or prototyping. Be aware that editable mode installation is not supported and you must have an OpenGL backend available on your system.
Install
dm-control on PyPI
Before you install
Low friction install with a wheel distribution available. Package is actively maintained with a recent release (17 days old) and an active repository. Note: cannot be installed in editable mode due to legacy auto-generated components; standard pip install required.
Requires at least one OpenGL rendering backend (GLFW, EGL, or OSMesa) installed on the system; GLFW requires a windowing system and cannot be used on headless machines. On Linux: libglfw3, libglew2.0, or libgl1-mesa-glx + libosmesa6 depending on backend choice.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for research and production applications.
Quickstart
pip install dm_control
import dm_control
from dm_control import suite
env = suite.load(domain_name='cartpole', task_name='balance')
action_spec = env.action_spec()
observation = env.reset()Verify before relying
- Whether the 15 runtime dependencies (including mujoco, numpy, scipy, lxml, pyopengl, glfw) are all required for basic usage or if some are optional for specific components.
- Performance characteristics and scalability limits for large-scale multi-agent simulations beyond the soccer tasks mentioned.
- Compatibility with specific MuJoCo versions beyond what the mujoco package dependency specifies.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesabsl-pydm-envdm-treeglfwlabmazelxmlmujoconumpyprotobufpyopenglpyparsingrequestssetuptoolsscipytqdm |
| Maintenance | Actively maintained 17 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 444,317 / month, #6,622 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dm_control-1.0.44-py3-none-any.whl
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