3 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
Structural Information-based Hierarchical Diffusion for Offline Reinforcement Learning
Xianghua Zeng, Hao Peng, Angsheng Li +1
Diffusion-based generative methods have shown promising potential for modeling trajectories from offline reinforcement learning (RL) datasets, and hierarchical diffusion has been i…
Effective Exploration Based on the Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
Traditional information theory provides a valuable foundation for Reinforcement Learning, particularly through representation learning and entropy maximization for agent exploratio…
Hierarchical Decision Making Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Dingli Su +1
Hierarchical Reinforcement Learning (HRL) is a promising approach for managing task complexity across multiple levels of abstraction and accelerating long-horizon agent exploration…
Semi-Supervised Clustering via Structural Entropy with Different Constraints
Guangjie Zeng, Hao Peng, Angsheng Li +4
Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Desp…