Towards Principled Representation Learning from Videos for Reinforcement Learning

Understand

We study pre-training representations for decision-making using video data, which is abundantly available for tasks such as game agents and software testing.

• Even though significant empirical advances have been made on this problem, a theoretical understanding remains absent.
• We initiate the theoretical investigation into principled approaches for representation learning and focus on learning the latent state representations of the underlying MDP using video data.
• We study two types of settings: one where there is iid noise in the observation, and a more challenging setting where there is also the presence of exogenous noise, which is non-iid noise that is temporally correlated, such as the motion of people or cars in the background.