30 citations · 30 across the 4 of their papers we have counts for
5 papers
Double Distillation Network for Multi-Agent Reinforcement Learning
Yang Zhou, Siying Wang, Wenyu Chen +3
Multi-agent reinforcement learning typically employs a centralized training-decentralized execution (CTDE) framework to alleviate the non-stationarity in environment. However, the…
Heterogeneous Value Decomposition Policy Fusion for Multi-Agent Cooperation
Siying Wang, Yang Zhou, Zhitong Zhao +4
Value decomposition (VD) has become one of the most prominent solutions in cooperative multi-agent reinforcement learning. Most existing methods generally explore how to factorize…
Optimistic ε-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning
Ruoning Zhang, Siying Wang, Wenyu Chen +5
The Centralized Training with Decentralized Execution (CTDE) paradigm is widely used in cooperative multi-agent reinforcement learning. However, conventional methods based on CTDE…
Large-scale Multi-view Subspace Clustering in Linear Time
Zhao Kang, Wangtao Zhou, Zhitong Zhao +3
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of vie…
Latent Multi-view Semi-Supervised Classification
Xiaofan Bo, Zhao Kang, Zhitong Zhao +2
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most…