google-deepmind/dm_control

Health v2 · maintenance only

Adoption not part of the score

4672 stars · 761 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Google DeepMind's Python stack for physics-based simulation and reinforcement learning environments built on the MuJoCo physics engine. It provides Python bindings to MuJoCo, a benchmark suite of continuous control tasks, an interactive viewer, and libraries for composing custom environments and multi-agent tasks.

Use cases

• train reinforcement learning agents on continuous control tasks
• simulate physics-based environments with MuJoCo in Python
• benchmark RL algorithms on standard control suites
• build custom RL environments from reusable components
• run multi-agent soccer locomotion tasks
• compose and modify MuJoCo MJCF models programmatically
• visualize physics environments interactively

When to choose

• you need well-established MuJoCo-based RL benchmark environments
• you want Python bindings and tooling around the MuJoCo physics engine
• you need to compose complex control tasks or multi-agent environments
• you are doing robotics or continuous control research

When to avoid

• you need GPU-accelerated massively parallel simulation (e.g. for fast RL training at scale)
• you work outside Python
• you need game-engine-quality rendering or non-robotics simulation
• you want editable pip installs of the package

Facets

library · maturity stable

simulation machine-learning reinforcement-learning graphics reinforcement-learning robotics machine-learning simulation python windows mujoco physics-engine rl-environments continuous-control deepmind mjcf locomotion multi-agent linux macos

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markdown · JSON · MCP: product_card(name="google-deepmind/dm_control")