DeepMind’s Dreamer AI learns from the past to predict the future

Some AI systems achieve goals in challenging environments by drawing on representations of the world informed by past experiences. They generalize these to novel situations, enabling them to complete tasks even in settings they haven’t encountered before. As it turns out, reinforcement learning — a training technique that employs rewards to drive software policies toward goals — is particularly well-suited to learning world models that summarize an agent’s experience, and by extension to facilitating the learning of novel behaviors.

Researchers hailing from Google, Alphabet subsidiary DeepMind, and the University of Toronto sought to exploit this with an agent — Dreamer — designed to internalize a world model and plan ahead to select actions by “imagining” their long-term outcomes. They say that it not only works for any learning objective, but that Dreamer exceeds existing approaches in data efficiency and computation time as well as final performance.