The content discusses an advanced algorithm for adversarial linear mixture MDPs, emphasizing unknown transitions and bandit feedback. The proposed method outperforms existing approaches by leveraging visit information from all states to estimate transition parameters accurately.
Significant advances have been made in reinforcement learning with linear function approximation, particularly in the context of adversarial losses. The paper introduces VLSUOB-REPS, a novel algorithm that improves upon existing methods by utilizing self-normalized concentration techniques to handle non-independent noises across different states.
The study addresses the challenges posed by unknown transitions and bandit feedback in linear mixture MDPs. By introducing innovative techniques from dynamic assortment problems, the algorithm bridges two distinct fields to enhance estimation accuracy and explore state orientations simultaneously.
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by Long-Fei Li,... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04568.pdfDeeper Inquiries