The DeepMind Control Suite was introduced in the paper of the same name, posted to arXiv on January 2, 2018 by Yuval Tassa and colleagues at DeepMind. It is a collection of continuous control tasks, such as balancing, walking, swimming, and manipulation, built on the MuJoCo physics engine and exposed through a uniform Python interface.
Its purpose is standardization. Before the suite, papers on continuous control often used slightly different task definitions, reward functions, and physics settings, which made results hard to compare across labs. The suite fixes a standardized structure with interpretable, normalized rewards on a consistent scale, so that a score on one task means the same thing in any paper that reports it, and it ships baseline results for several learning algorithms.
The suite became one of the default proving grounds for continuous control methods, including Soft Actor-Critic, TD3, and the Dreamer family of agents. For a general reader, it is another example of shared infrastructure quietly accelerating a field: agreeing on how to measure progress is a prerequisite for making it.