Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning

• URL: http://arxiv.org/abs/2201.07016v1
• Date: Tue, 18 Jan 2022 14:28:30 GMT
• Title: Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning
• Authors: Tao Huang, Jiachen Wang, Xiao Chen
• Abstract summary: We propose to accelerate state representation learning by enforcing view-consistency on the dynamics. 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.
• Score: 12.485293708638292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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