The study focuses on the sample complexity of learning optimal policies in weakly communicating and general average reward Markov decision processes (MDPs). The research establishes minimax optimal bounds for both settings, improving existing work. By reducing the average-reward MDP to a discounted MDP, new insights are gained, leading to improved sample complexity results. The analysis involves bounding variance parameters and leveraging reductions to optimize learning efficiency.
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by Matthew Zure... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2403.11477.pdfDeeper Inquiries