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arXiv Machine Learning By John J. Vastola, Kanaka Rajan

Solvable models of learning to pursue a moving target

Read the original on arXiv Machine Learning →

The paper introduces a continuous‑time linear‑quadratic model for agents learning to intercept a moving target, offering optimal control strategies that span reactive and predictive extremes. The model’s few interpretable parameters allow closed‑form solutions for optimal strategies, policy learning dynamics, and value learning dynamics. Analysis of the model’s eigenvalue spectra reveals a gap that explains why agents typically learn to reach the target before fully optimizing their movement.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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