SOURCE-LINKED INTELLIGENCE

A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Classical dynamic programming represents optimal sequential decisions through value functions and policies. While this functional representation is natural for computing optimal decisions, it does not directly identify the mathematical object governing policy reconstruction, representation complexity, or oracle-query complexity once an optimal policy is fixed. This paper addresses this question by developing a geometric theory of structured optimal policies in which the decision-boundary geometry induced by the policy becomes the primary object of analysis. We show that, under suitable structu

Read original source ↗ Open in workspace

recordType: paper
region: Global

Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-16T13:01:13.000Z

First collected: 2026-09-20T08:01:03.945Z. This is not the publication date.