Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control

Understand

We address the problem of learning reusable state representations from streaming high-dimensional observations.

• This is important for areas like Reinforcement Learning (RL), which yields non-stationary data distributions during training.
• We make two key contributions.
• First, we propose an evaluation suite that measures alignment between latent and true low-dimensional states.