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. The world model uses discrete representations and is trained separately from the policy. DreamerV2 constitutes the first agent that achieves human-level performance on the Atari benchmark of 55 tasks by learning behaviors inside a separately trained world model. With the same computational budget and wall-clock time, DreamerV2 reaches 200M frames and exceeds the final performance of the top single-GPU agents IQN and Rainbow.

DreamerV2是一种基于world models且在离线的训练集上通过reinforcement learning进行策略(即行为)学习的智能体，用于通过对compact latent space进行预测，实现从Atari游戏或连续动作任务的图像输入中进行行为学习的高效模型，实现了在55个任务中的human-level的性能，获得了与IQN和Rainbow类似的结果。

离散世界模型掌握Atari