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arXiv · 2506.01145

Slow Feature Analysis on Markov Chains from Goal-Directed Behavior

Merlin Schüler·Eddie Seabrook·Laurenz Wiskott
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Abstract

Slow Feature Analysis is a unsupervised representation learning method that extracts slowly varying features from temporal data and can be used as a basis for subsequent reinforcement learning. Often, the behavior that generates the data on which the representation is learned is assumed to be a uniform random walk. Less research has focused on using samples generated by goal-directed behavior, as commonly the case in a reinforcement learning setting, to learn a representation. In a spatial setting, goal-directed behavior typically leads to significant differences in state occupancy between states that are close to a reward location and far from a reward location. Through the perspective of optimal slow features on ergodic Markov chains, this work investigates the effects of these differences on value-function approximation in an idealized setting. Furthermore, three correction routes, which can potentially alleviate detrimental scaling effects, are evaluated and discussed. In addition, the special case of goal-averse behavior is considered.

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Merlin Schüler, Eddie Seabrook, Laurenz Wiskott. 2025-06-01. Slow Feature Analysis on Markov Chains from Goal-Directed Behavior. https://arxiv.org/abs/2506.01145

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