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News/Adaptive Exploration for Latent-State Bandits
arxiv
PublishedJune 2, 2026 at 4:00 AM
—neutral

Adaptive Exploration for Latent-State Bandits

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

arXiv:2602.05139v3 Announce Type: replace Abstract: We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions. The optimal arm can change even though the learner observes only past actions and rewards. We propose algorithms that feed LinU

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    LinUCB
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arxiv
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#bandits#machine-learning#markov-state

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Mentioned models
01
  • 01
    LinUCB
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arxiv
Read original ↗All from arxiv →
Tags
03
#bandits#machine-learning#markov-state

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arxivVeriEvolve: Evaluator-First Evolution for Tool-Using Agents1darxivTNODEV: Toolbox for Neural ODE Verification1darxivLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding1darxivOn the Expressive Power and Limitations of Multi-Layer SSMs1d
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