Mastering Atari with Discrete World Models

Understand

Intelligent agents need to generalize from past experience to achieve goals in complex environments.

• World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency.
• While learning world models from image inputs has recently become feasible for some tasks, modeling Atari games accurately enough to derive successful behaviors has remained an open challenge for many years.
• We introduce DreamerV2, a reinforcement learning agent that learns behaviors purely from predictions in the compact latent space of a powerful world model.