6 citations · 8 across the 12 of their papers we have counts for
13 papers
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…
Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
Diffusion models have shown promising capabilities in trajectory generation for planning in offline reinforcement learning (RL). However, conventional diffusion-based planning meth…
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…
Safety Modulation: Enhancing Safety in Reinforcement Learning through Cost-Modulated Rewards
Hanping Zhang, Yuhong Guo
Safe Reinforcement Learning (Safe RL) aims to train an RL agent to maximize its performance in real-world environments while adhering to safety constraints, as exceeding safety vio…
Zero-Shot Action Generalization with Limited Observations
Abdullah Alchihabi, Hanping Zhang, Yuhong Guo
Reinforcement Learning (RL) has demonstrated remarkable success in solving sequential decision-making problems. However, in real-world scenarios, RL agents often struggle to genera…
Skill-Enhanced Reinforcement Learning Acceleration from Heterogeneous Demonstrations
Hanping Zhang, Yuhong Guo
Learning from Demonstration (LfD) is a well-established problem in Reinforcement Learning (RL), which aims to facilitate rapid RL by leveraging expert demonstrations to pre-train t…
Reinforcement Learning-Guided Semi-Supervised Learning
Marzi Heidari, Hanping Zhang, Yuhong Guo
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especi…
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
Marzi Heidari, Hanping Zhang, Yuhong Guo
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL…
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation
Hanping Zhang, Yuhong Guo
The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments.…