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cs.LG2025
Semi-pessimistic Reinforcement Learning
Jin Zhu, Xin Zhou, Jiaang Yao +5
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected data. However, it faces challenges of distributional shift, where the learned policy may enco…
cs.LG2023
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
Jin Zhu, Runzhe Wan, Zhengling Qi +2
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applicati…