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Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
[article]
2020
arXiv
pre-print
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. We benchmark several widely used unsupervised learning approaches. This uncovers the strengths and limitations of
arXiv:2006.08718v2
fatcat:ag2hswmjbzfrpfnhnf7h24whqe