18 citations · 39 across the 5 of their papers we have counts for
5 papers
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization
Shixuan Liu, Yanghe Feng, Keyu Wu +3
In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle with unoriented edges or latent assumpti…
PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay
Xingxing Liang, Yang Ma, Yanghe Feng +1
On-policy deep reinforcement learning algorithms have low data utilization and require significant experience for policy improvement. This paper proposes a proximal policy optimiza…
Finding spin glass ground states through deep reinforcement learning
Changjun Fan, Mutian Shen, Zohar Nussinov +3
Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for t…
Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach
Changjun Fan, Li Zeng, Yuhui Ding +3
Betweenness centrality (BC) is one of the most used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction…
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control
Xingxing Liang, Qi Wang, Yanghe Feng +2
Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bott…