19 citations · 32 across the 7 of their papers we have counts for
4 papers · 1 filter
Hyper-Decision Transformer for Efficient Online Policy Adaptation
Mengdi Xu, Yuchen Lu, Yikang Shen +3
Decision Transformers (DT) have demonstrated strong performances in offline reinforcement learning settings, but quickly adapting to unseen novel tasks remains challenging. To addr…
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu +8
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Peide Huang, Mengdi Xu, Jiacheng Zhu +3
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks. In this work, we focus on the id…
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
Mengdi Xu, Zuxin Liu, Peide Huang +4
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} cons…