Source: https://ai.googleblog.com/2021/07/speeding-up-reinforcement-learning-with.html
Reinforcement learning (RL) is a popular method for teaching robots to navigate and manipulate the physical world, which itself can be simplified and expressed as interactions between rigid bodies1 (i.e., solid physical objects that do not deform when a force is applied to them). In order to facilitate the collection of training data in a practical amount of time, RL usually leverages simulation, where approximations of any number of complex objects are composed of many rigid bodies connected by joints and powered by actuators. But this poses a challenge: it frequently takes millions to billions of simulation frames for an RL agent to become proficient at even simple tasks, such as walking, using tools, or assembling toy blocks.