解析的潜在関係学習：連続制御のための表現の統一と評価
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
高次元観測から再利用可能な状態表現を学ぶため、潜在空間と真の低次元状態の整合性を測る評価スイートを提案し、潜在関係を学習する統一的な数式定式化を導入して、源領域で学んだ関係を目標領域の潜在空間構造化に転移できることを示した。
著者: Rika Antonova, Maksim Maydanskiy, Danica Kragic, Sam Devlin, Katja Hofmann
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
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 existing approaches that impose additional constraints/objectives on the latent space. Our second contribution is a unifying mathematical formulation for learning latent relations. We learn analytic relations on source domains, then use these relations to help structure the latent space when learning on target domains. This formulation enables a more general, flexible and principled way of shaping the latent space. It formalizes the notion of learning independent relations, without imposing restrictive simplifying assumptions or requiring domain-specific information. We present mathematical properties, concrete algorithms for implementation and experimental validation of successful learning and transfer of latent relations.
