A Geometric Theory of Decision Boundaries in Structured Markov Decision Processes

arXiv:2609.18610 · cs.LG, math.OC · Submitted 2026-09-16 · Read on arXiv

cs.LG, math.OC

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/phdPokou/A-Geometric-Theory-of-Decision-Boundaries

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Sample Complexity — 26× in this paper · explained in 15 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Robustness — 16× in this paper · explained in 5 episodes
• Decision Compression Ratio — 15× in this paper
• Action Space — 13× in this paper · explained in 1 episode
• Generalization — 6× in this paper · explained in 13 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Scaling Laws — 6× in this paper · explained in 4 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Ambient Dimension — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Minimum Description Length — 2× in this paper
• Query Complexity — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

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