The DeepMind Control Suite is a collection of continuous control tasks designed as standardized benchmarks for evaluating reinforcement learning (RL) algorithms. These tasks are implemented in Python and powered by the MuJoCo physics engine, making them easy to use and modify. The suite includes a variety of domains such as Acrobot, Cart-pole, Cheetah, Finger, Fish, Hopper, Humanoid, Manipulator, Pendulum, Point-mass, Reacher, Swimmer, and Walker, with detailed descriptions of each task's state, action, observation, and reward structures. The tasks are designed to be interpretable, with a unified reward structure that allows for consistent performance evaluation across the suite. The suite also includes a set of benchmarking tasks and additional tasks that are more challenging or do not conform to the standard structure. The Control Suite provides high-level and low-level Python APIs for interacting with the tasks, and includes benchmarking results for various RL algorithms such as A3C, DDPG, and D4PG. The suite is publicly available on GitHub and includes a video summary of all tasks. The Control Suite aims to provide a standardized, well-documented, and extensible platform for evaluating and comparing RL algorithms in continuous control settings.The DeepMind Control Suite is a collection of continuous control tasks designed as standardized benchmarks for evaluating reinforcement learning (RL) algorithms. These tasks are implemented in Python and powered by the MuJoCo physics engine, making them easy to use and modify. The suite includes a variety of domains such as Acrobot, Cart-pole, Cheetah, Finger, Fish, Hopper, Humanoid, Manipulator, Pendulum, Point-mass, Reacher, Swimmer, and Walker, with detailed descriptions of each task's state, action, observation, and reward structures. The tasks are designed to be interpretable, with a unified reward structure that allows for consistent performance evaluation across the suite. The suite also includes a set of benchmarking tasks and additional tasks that are more challenging or do not conform to the standard structure. The Control Suite provides high-level and low-level Python APIs for interacting with the tasks, and includes benchmarking results for various RL algorithms such as A3C, DDPG, and D4PG. The suite is publicly available on GitHub and includes a video summary of all tasks. The Control Suite aims to provide a standardized, well-documented, and extensible platform for evaluating and comparing RL algorithms in continuous control settings.