New framework enhances robust decision-making in uncertain environments

Researchers have developed a new framework for robust average-reward Markov decision processes, which are used for sequential decision-making under uncertainty. The study quantifies the necessary and sufficient number of samples required to learn an optimal robust policy. The findings reveal that the sample complexity depends on the perturbation scale and the optimal bias spans, with distinct regimes for high and low tolerance. The proposed plug-in procedures achieve these rates by selecting appropriate reductions and discount factors, with options for both span-informed and span-agnostic calibration from data. AI

IMPACT Enhances theoretical understanding of decision-making under uncertainty, potentially impacting AI agents in complex environments.

RANK_REASON This is a research paper published on arXiv detailing a new theoretical framework for Markov decision processes. [lever_c_demoted from research: ic=1 ai=1.0]

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