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.
@article{arxiv.2201.07016,
title = {Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning},
author = {Tao Huang and Jiachen Wang and Xiao Chen},
journal= {arXiv preprint arXiv:2201.07016},
year = {2022}
}