Learning to Model the World with Language
Jessy Lin Yuqing Du Olivia Watkins Danijar Hafner Pieter Abbeel Dan Klein Anca Dragan
To interact with humans in the world, agents need to understand the diverse
types of language that people use, relate them to the visual world, and act
based on them. While current agents learn to execute simple language
instructions from task rewards, we aim to build agents that leverage diverse
language that conveys general knowledge, describes the state of the world,
provides interactive feedback, and more. Our key idea is that language helps
agents predict the future: what will be observed, how the world will behave,
and which situations will be rewarded. This perspective unifies language
understanding with future prediction as a powerful self-supervised learning
objective. We present Dynalang, an agent that learns a multimodal world model
that predicts future text and image representations and learns to act from
imagined model rollouts. Unlike traditional agents that use language only to
predict actions, Dynalang acquires rich language understanding by using past
language also to predict future language, video, and rewards. In addition to
learning from online interaction in an environment, Dynalang can be pretrained
on datasets of text, video, or both without actions or rewards. From using
language hints in grid worlds to navigating photorealistic scans of homes,
Dynalang utilizes diverse types of language to improve task performance,
including environment descriptions, game rules, and instructions.