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cs.LG2025
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures
Ming Yin, Mengdi Wang, Yu-Xiang Wang
This article reviews the recent advances on the statistical foundation of reinforcement learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline…
cs.LG2023
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Sunil Madhow, Dan Qiao, Ming Yin +1
Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable. Currently, most r…