5 citations · 11 across the 8 of their papers we have counts for
8 papers
Multi-Agent Cooperation via Unsupervised Learning of Joint Intentions
Shanqi Liu, Weiwei Liu, Wenzhou Chen +2
The field of cooperative multi-agent reinforcement learning (MARL) has seen widespread use in addressing complex coordination tasks. While value decomposition methods in MARL have…
Controlling Type Confounding in Ad Hoc Teamwork with Instance-wise Teammate Feedback Rectification
Dong Xing, Pengjie Gu, Qian Zheng +5
Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior coordination. Many works propose to abstract teammate instances into high-level representation o…
An Adaptive Multi-Level Max-Plus Method for Deterministic Optimal Control Problems
Marianne Akian, Stéphane Gaubert, Shanqing Liu
We introduce a new numerical method to approximate the solution of a finite horizon deterministic optimal control problem. We exploit two Hamilton-Jacobi-Bellman PDE, arising by co…
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning
Shanqi Liu, Yujing Hu, Runze Wu +5
Real-world cooperation often requires intensive coordination among agents simultaneously. This task has been extensively studied within the framework of cooperative multi-agent rei…
The Onsager-Machlup action functional for Mckean-Vlasov SDEs
Shanqi Liu, Hongjun Gao, Huijie Qiao
This paper is devoted to deriving the Onsager-Machlup action functional for Mckean-Vlasov stochastic differential equations in a class of norms that dominate $L^2([0,1], \mathbb{R}…
One-shot Face Reenactment Using Appearance Adaptive Normalization
Guangming Yao, Yi Yuan, Tianjia Shao +5
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a dri…
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together
Shanqi Liu, Licheng Wen, Jinhao Cui +3
Multi-agent path finding in formation has many potential real-world applications like mobile warehouse robots. However, previous multi-agent path finding (MAPF) methods hardly take…
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations
Shanqi Liu, Junjie Cao, Wenzhou Chen +2
It is challenging learning from demonstrated observation-only trajectories in a non-time-aligned environment because most imitation learning methods aim to imitate experts by follo…