Learning informative representations from image-based observations is of
fundamental concern in deep Reinforcement Learning (RL). However,
data-inefficiency remains a significant barrier to this objective. To overcome
this obstacle, we propose to accelerate state representation learning by
enforcing view-consistency on the dynamics. Firstly, we introduce a formalism
of Multi-view Markov Decision Process (MMDP) that incorporates multiple views
of the state. Following the structure of MMDP, our method, View-Consistent
Dynamics (VCD), learns state representations by training a view-consistent
dynamics model in the latent space, where views are generated by applying data
augmentation to states. Empirical evaluation on DeepMind Control Suite and
Atari-100k demonstrates VCD to be the SoTA data-efficient algorithm on visual
control tasks.

通过提出多视角马尔可夫决策过程和视图一致的动力学模型，在深度强化学习中学习图像观测的信息表示，以达到数据高效的目的。该模型在 DeepMind Control Suite 和 Atari-100k 数据集上的实验验证表明，其是视觉控制任务最先进的数据高效算法。