The content discusses the challenges in offline reinforcement learning due to discrepancies between simulated and deployed environments. It introduces the concept of distributionally robust linear Markov decision processes (MDPs) and presents the DROP algorithm to address sample complexity issues. The algorithm incorporates variance estimation to improve performance guarantees. The paper provides theoretical guarantees and sample complexity analysis under different data coverage scenarios.
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by He Wang,Laix... at arxiv.org 03-20-2024
https://arxiv.org/pdf/2403.12946.pdfDeeper Inquiries