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20222026
most citedDouble Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage

2 citations · 4 across the 9 of their papers we have counts for

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cs.LG2023

Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration

Zhihan Liu, Miao Lu, Wei Xiong +6

In online reinforcement learning (online RL), balancing exploration and exploitation is crucial for finding an optimal policy in a sample-efficient way. To achieve this, existing s…

cs.LG2023★ 2 cited

Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage

Jose Blanchet, Miao Lu, Tong Zhang +1

In this paper, we study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal policy purely from an offline dataset that can pe…