Training Agents Inside of Scalable World Models

Understand

World models learn general knowledge from videos and simulate experience for training behaviors in imagination, offering a path towards intelligent agents.

• However, previous world models have been unable to accurately predict object interactions in complex environments.
• We introduce Dreamer 4, a scalable agent that learns to solve control tasks by reinforcement learning inside of a fast and accurate world model.
• In the complex video game Minecraft, the world model accurately predicts object interactions and game mechanics, outperforming previous world models by a large margin.