7 citations · 29 across the 13 of their papers we have counts for
13 papers
Learning to Coordinate with Anyone
Lei Yuan, Lihe Li, Ziqian Zhang +5
In open multi-agent environments, the agents may encounter unexpected teammates. Classical multi-agent learning approaches train agents that can only coordinate with seen teammates…
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
Yuhang Ran, Yi-Chen Li, Fuxiang Zhang +2
We consider the problem of learning the best possible policy from a fixed dataset, known as offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is p…
Language Model Self-improvement by Reinforcement Learning Contemplation
Jing-Cheng Pang, Pengyuan Wang, Kaiyuan Li +4
Large Language Models (LLMs) have exhibited remarkable performance across various natural language processing (NLP) tasks. However, fine-tuning these models often necessitates subs…
Fast Teammate Adaptation in the Presence of Sudden Policy Change
Ziqian Zhang, Lei Yuan, Lihe Li +5
In cooperative multi-agent reinforcement learning (MARL), where an agent coordinates with teammate(s) for a shared goal, it may sustain non-stationary caused by the policy change o…
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
Lei Yuan, Zi-Qian Zhang, Ke Xue +6
Cooperative multi-agent reinforcement learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via…
Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation
Lei Yuan, Feng Chen, Zhongzhang Zhang +1
Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning (MARL). Nowadays, existing works mainly focus on improving the communication efficiency of…
Multi-agent Continual Coordination via Progressive Task Contextualization
Lei Yuan, Lihe Li, Ziqian Zhang +3
Cooperative Multi-agent Reinforcement Learning (MARL) has attracted significant attention and played the potential for many real-world applications. Previous arts mainly focus on f…
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
Xu-Hui Liu, Feng Xu, Xinyu Zhang +5
Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from…
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning
Cong Guan, Feng Chen, Lei Yuan +2
Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). Previous works typically combine raw messages of teammates with…
Multi-agent Dynamic Algorithm Configuration
Ke Xue, Jiacheng Xu, Lei Yuan +4
Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (D…
Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
Ke Xue, Yutong Wang, Cong Guan +5
Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some stu…
Model Generation with Provable Coverability for Offline Reinforcement Learning
Chengxing Jia, Hao Yin, Chenxiao Gao +4
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could ad…
Multi-Agent Policy Transfer via Task Relationship Modeling
Rongjun Qin, Feng Chen, Tonghan Wang +5
Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous work on multi-agent transfer learning…