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New algorithms yield data- and variance-dependent regret bounds for MDPs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for online tabular Markov decision processes (MDPs) that offer improved regret bounds. These algorithms adapt to data-dependent measures in adversarial settings and variance-dependent measures in stochastic settings. The work introduces novel complexity measures and optimistic optimization techniques, achieving near-optimal regret bounds. AI

IMPACT Introduces refined theoretical bounds for reinforcement learning algorithms, potentially improving agent performance in complex environments.

RANK_REASON The cluster contains an academic paper detailing new algorithms and theoretical bounds for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mingyi Li, Taira Tsuchiya, Kenji Yamanishi ·

    Data- and Variance-dependent Regret Bounds for Online Tabular MDPs

    arXiv:2602.01903v2 Announce Type: replace-cross Abstract: This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent regret bounds in the adversarial regime and …

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