World Model

In depth

A learned model of an environment that predicts how it will change in response to actions, letting an agent simulate outcomes before acting. Ha and Schmidhuber's 2018 paper trained a generative model to compress an environment into a latent representation, then trained a policy entirely inside the model's own simulated "dream" and transferred it back to the real environment. The term now also covers large video and multimodal models pitched as general simulators of the physical world for robotics and interactive generation.

Primary source: arxiv.org