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News/Toward Learning POMDPs Beyond Full-Rank Actions and State Observability
arxiv
PublishedJuly 22, 2026 at 4:00 AM
—neutral

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2601.18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast this problem as learning the parameters of a discrete Partially Observable Markov Decision Process (POMD

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Discussion
Mentioned models
01
  • 01
    Predictive State Representations (PSRs)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#machine-learning#pomdp#autonomous-agents

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Mentioned models
01
  • 01
    Predictive State Representations (PSRs)
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#machine-learning#pomdp#autonomous-agents

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arxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents1darxivTNODEV: Toolbox for Neural ODE Verification1d
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