1 citations · 2 across the 11 of their papers we have counts for
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Select-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance
Hanping Zhang, Adam Koziak, Yuhong Guo
Reinforcement Learning (RL) has been widely applied to sequential decision-making, yet it often suffers from poor sample efficiency due to costly interactions with the environment.…
Bi-Level Optimization for Single Domain Generalization
Marzi Heidari, Hanping Zhang, Hao Yan +1
Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains a fundamental challenge in robust machine lear…
Bridging Dynamics Gaps via Diffusion Schrödinger Bridge for Cross-Domain Reinforcement Learning
Hanping Zhang, Yuhong Guo
Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains. A key challenge lies in the lack of target-dom…
Learning to Clean: Reinforcement Learning for Noisy Label Correction
Marzi Heidari, Hanping Zhang, Yuhong Guo
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This pape…
LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning
Hanping Zhang, Yuhong Guo
Reinforcement Learning (RL) is known for its strong decision-making capabilities and has been widely applied in various real-world scenarios. However, with the increasing availabil…
Skill-based Safe Reinforcement Learning with Risk Planning
Hanping Zhang, Yuhong Guo
Safe Reinforcement Learning (Safe RL) aims to ensure safety when an RL agent conducts learning by interacting with real-world environments where improper actions can induce high co…