{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"dm_control provides Python bindings to the MuJoCo physics engine and a suite of reinforcement learning environments for continuous control tasks, along with tools for composing and modifying physics-based simulations.","skillfed_tags":["physics-simulation","reinforcement-learning","robotics-sim"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"dm-control","links":{"html":"https://skillfed.io/packages/dm-control","md":"https://skillfed.io/packages/dm-control.md","pypi":"https://pypi.org/project/dm-control/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":null,"license_treatment":"permissive","name":"dm-control","python_support":"supports_current","summary":"Continuous control environments and MuJoCo Python bindings."},"popularity":{"monthly_downloads":444317,"position":6622,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.44"}