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POMDP value functions characterized as semi-algebraic sets

By PulseAugur Editorial · [2 sources] ·

Researchers have characterized the feasible set of value functions in partially observable Markov decision processes (POMDPs) as a semi-algebraic set. This extends previous work on fully observable processes, revealing that partial observability introduces nonlinear constraints and a more complex geometric structure. The findings offer new insights into policy optimization and highlight unique phenomena in POMDPs, such as the potential for isolated local reward maximizers. AI

IMPACT Provides theoretical groundwork for advanced AI decision-making systems in uncertain environments.

RANK_REASON The cluster contains an academic paper detailing a theoretical advancement in a specific area of mathematics and computer science.

Read on arXiv stat.ML →

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POMDP value functions characterized as semi-algebraic sets

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Ryan A. Anderson, Guido Montufar ·

    The Value Function Semi-Algebraic Set in Partially Observable Markov Decision Processes

    arXiv:2606.03048v1 Announce Type: cross Abstract: We study the geometry of feasible value functions in infinite-horizon partially observable Markov decision processes (POMDPs) under memoryless stochastic policies. Our main contribution is a characterization of the feasible set of…

  2. arXiv stat.ML TIER_1 English(EN) · Guido Montufar ·

    The Value Function Semi-Algebraic Set in Partially Observable Markov Decision Processes

    We study the geometry of feasible value functions in infinite-horizon partially observable Markov decision processes (POMDPs) under memoryless stochastic policies. Our main contribution is a characterization of the feasible set of value functions as a semi-algebraic set, defined …

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