World Models is a research project focused on building generative neural network models of reinforcement learning environments. Their core innovation is a world model that learns compressed spatial and temporal representations, allowing agents to be trained within simulated "dreams" and then transferred to actual environments. This technology enables the development of compact and simple policies for agents.