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Qixian Yu

2 accepted papers

2025

Improving Generalization in Offline Reinforcement Learning via Latent Distribution Representation Learning

AAAI 2025technical

Dealing with the distribution shift is a significant challenge when building offline reinforcement learning (RL) models that can generalize from a static dataset to out-of-distribution (OOD) scenarios. Previous approaches have employed pessimism or conservatism strategies. More recently, data-driven…

Cited by 0SourcePDFScholar
2024

Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting

ICML 2024poster

Offline Reinforcement Learning (RL) commonly suffers from the out-of-distribution (OOD) overestimation issue due to the distribution shift. Prior work gradually shifts their focus from suppressing OOD overestimation to avoiding overly conservative learning from suboptimal behavior policies to improv…