1 citations · 2 across the 4 of their papers we have counts for
6 papers
Subgoal Graph-Augmented Planning for LLM-Guided Open-World Reinforcement Learning
Shanwei Fan, Bin Zhang, Zhiwei Xu +4
Large language models (LLMs) offer strong high-level planning capabilities for reinforcement learning (RL) by decomposing tasks into subgoals. However, their practical utility is l…
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Hangyu Mao, Nianmin Zhang +8
In partially observable multi-agent systems, agents typically only have access to local observations. This severely hinders their ability to make precise decisions, particularly du…
Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning
Zhihao Liu, Xianliang Yang, Zichuan Liu +7
Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that can learn to adopt cooperative or competitive strategies within complex environments. Howeve…
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning
Dapeng Li, Hang Dong, Lu Wang +8
In recent years, multi-agent reinforcement learning algorithms have made significant advancements in diverse gaming environments, leading to increased interest in the broader appli…
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Dapeng Li, Yunpeng Bai +1
In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations. To solve such problems, many multi-agent reinforcement…
Learning to Coordinate via Multiple Graph Neural Networks
Zhiwei Xu, Bin Zhang, Yunpeng Bai +2
The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper intr…