5 citations · 15 across the 8 of their papers we have counts for
8 papers
CORD: Generalizable Cooperation via Role Diversity
Kanefumi Matsuyama, Kefan Su, Jiangxing Wang +2
Cooperative multi-agent reinforcement learning (MARL) aims to develop agents that can collaborate effectively. However, most cooperative MARL methods overfit training agents, makin…
AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games
Kefan Su, Yusen Huo, Zhilin Zhang +5
Decision-making in large-scale games is an essential research area in artificial intelligence (AI) with significant real-world impact. However, the limited access to realistic larg…
Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
Jiechuan Jiang, Kefan Su, Zongqing Lu
Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the a…
Decentralized Policy Optimization
Kefan Su, Zongqing Lu
The study of decentralized learning or independent learning in cooperative multi-agent reinforcement learning has a history of decades. Recently empirical studies show that indepen…
Multi-Agent Automated Machine Learning
Zhaozhi Wang, Kefan Su, Jian Zhang +4
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML). MA…
Multi-Agent Coordination via Multi-Level Communication
Ziluo Ding, Zeyuan Liu, Zhirui Fang +3
The partial observability and stochasticity in multi-agent settings can be mitigated by accessing more information about others via communication. However, the coordination problem…
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Kefan Su, Siyuan Zhou, Jiechuan Jiang +3
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL). However, non-stationarity remains a significant challenge in fully decentr…
Divergence-Regularized Multi-Agent Actor-Critic
Kefan Su, Zongqing Lu
Entropy regularization is a popular method in reinforcement learning (RL). Although it has many advantages, it alters the RL objective of the original Markov Decision Process (MDP)…