Researchers have developed a new model-based bootstrap method for controlled Markov chains, particularly useful in offline reinforcement learning scenarios where the data-generating policy is unknown. This technique establishes distributional consistency for transition estimators and extends to policy evaluation and recovery, providing asymptotically valid confidence intervals for value and Q-functions. Experimental results on the RiverSwim problem demonstrate that the proposed confidence intervals offer improved calibration and coverage compared to existing methods, especially with limited data. AI
影响 Improves confidence interval calibration for offline reinforcement learning, aiding in more reliable policy evaluation and recovery.
排序理由 The cluster contains an academic paper detailing a new statistical method for controlled Markov chains, relevant to reinforcement learning.
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