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New research details complexity gap between POMDPs and multi-environment POMDPs

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the computational complexity of Partially Observable Markov Decision Processes (POMDPs) and their multi-environment counterparts (ME-POMDPs). The study identifies specific subclasses of POMDPs that retain PSPACE-completeness when extended to the multi-environment setting, even if they are tractable in the single-environment case. The research also establishes NP-completeness for multi-environment blind MDPs and deterministic POMDPs, and reveals existential theory of the reals (∃R)-completeness for certain ME-POMDP problems under time-dependent policies. AI

IMPACT This research contributes to the theoretical understanding of decision-making under uncertainty in AI, potentially impacting the design of more robust and efficient algorithms for complex environments.

RANK_REASON The cluster contains a single academic paper detailing theoretical computational complexity results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details complexity gap between POMDPs and multi-environment POMDPs

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The cluster contains a single academic paper detailing theoretical computational complexity results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Eline M. Bovy, Caleb Probine, Marnix Suilen, Ufuk Topcu, Nils Jansen ·

    On the Complexity Gap Between POMDPs and Multi-Environment POMDPs

    arXiv:2610.15010v1 Announce Type: new Abstract: Multi-environment POMDPs (ME-POMDPs) generalize POMDPs by encoding a finite set of possible environments, capturing model uncertainty. The finite-horizon robust reward maximization problem for a ME-POMDP asks for a policy that maxim…

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