Introduces Good Policy Identification (GPI) and BEE-GPI algorithm whose sample complexity for positive instances has log(1/δ) coefficient O(H²/(V*−μ0)²) independent of state and action space sizes.
Journal of Machine Learning Research , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new posterior sampling algorithm for (ε, δ)-PAC policy identification in tabular MDPs achieves asymptotic optimality in sample complexity and posterior contraction rate with O(S²AH) runtime per episode.
citing papers explorer
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Pure Exploration for a Good Policy in Reinforcement Learning with Bandit Feedback
Introduces Good Policy Identification (GPI) and BEE-GPI algorithm whose sample complexity for positive instances has log(1/δ) coefficient O(H²/(V*−μ0)²) independent of state and action space sizes.
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Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes
A new posterior sampling algorithm for (ε, δ)-PAC policy identification in tabular MDPs achieves asymptotic optimality in sample complexity and posterior contraction rate with O(S²AH) runtime per episode.