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New minimax PAC bounds for learning in exogenous contextual MDPs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new minimax PAC bounds for learning in exogenous contextual Markov decision processes (MDPs). The study focuses on tabular discounted MDPs with exogenous, i.i.d. contexts that can influence rewards and transitions. The proposed algorithms offer improved sample complexity for policy evaluation, best-value estimation, and best-policy extraction, with rates that are independent of the context space size and are minimax optimal. AI

IMPACT Establishes theoretical bounds for learning in complex sequential decision-making environments, potentially improving AI agent capabilities in uncertain, context-dependent scenarios.

RANK_REASON The cluster contains a research paper detailing theoretical advancements in machine learning for Markov decision processes.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New minimax PAC bounds for learning in exogenous contextual MDPs

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet ·

    Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

    arXiv:2606.25170v1 Announce Type: new Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor $\gamma$, finite state space $\mathcal X$, action space $\mathcal A$, and context space $\mathcal Z$. At each…

  2. arXiv stat.ML TIER_1 English(EN) · Vianney Perchet ·

    Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

    We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor $γ$, finite state space $\mathcal X$, action space $\mathcal A$, and context space $\mathcal Z$. At each time step, a context is drawn independently from an …

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