  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.
