google-deepmind/dm_control

dm_control is a Python package from Google DeepMind that provides tools for building and running physics-based simulations, primarily for training AI systems through reinforcement learning. Reinforcement learning is a method where an AI agent learns to make decisions by taking actions in an environment and receiving feedback (rewards or penalties) based on the results. To train such an agent, you need a simulated environment where the agent can practice, which is what this package provides. The physics simulation runs on top of MuJoCo, a physics engine developed for robotics and biomechanics research. MuJoCo calculates how simulated objects, joints, and bodies move and interact in response to applied forces. This package wraps MuJoCo with a Python interface and adds a collection of pre-built environments, called the Control Suite, that include tasks like controlling a simulated robotic arm, a walking creature, a ball-in-cup catch, and other classic continuous control problems. Each task comes with a defined observation (what the agent can see) and a reward signal (what counts as doing well). Beyond the pre-built suite, the package includes a library called MJCF that lets you build and modify simulated robot bodies in Python code, and a Composer library for assembling more complex multi-part tasks from reusable pieces. There is also a multi-agent soccer environment where several simulated players compete on a field. Installation is via pip (the standard Python package manager) and does not require any steps beyond that command, though rendering 3D scenes requires one of three graphics backends to be available on your system. The package supports headless rendering on servers without a display, which is important for running experiments on cloud machines. An introductory tutorial is available as a runnable notebook in Google Colab. The package is accompanied by a peer-reviewed publication in the journal Software Impacts.