4 citations · 20 across the 22 of their papers we have counts for
22 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…
Mastering Complex Coordination through Attention-based Dynamic Graph
Guangchong Zhou, Zhiwei Xu, Zeren Zhang +1
The coordination between agents in multi-agent systems has become a popular topic in many fields. To catch the inner relationship between agents, the graph structure is combined wi…
Adaptive parameter sharing for multi-agent reinforcement learning
Dapeng Li, Na Lou, Bin Zhang +2
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems. However, the effectiveness of parame…
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
Bin Zhang, Hangyu Mao, Jingqing Ruan +9
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS). However, as the numb…
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Bin Zhang, Hangyu Mao, Lijuan Li +4
Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of exist…
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Dapeng Li, Zhiwei Xu, Bin Zhang +1
Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks. However, partial observability i…
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning
Dapeng Li, Zhiwei Xu, Bin Zhang +1
Spatial information is essential in various fields. How to explicitly model according to the spatial location of agents is also very important for the multi-agent problem, especial…
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning
Bin Zhang, Lijuan Li, Zhiwei Xu +2
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure. However, existing MARL a…
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
Dapeng Li, Feiyang Pan, Jia He +3
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable. For example, vo…
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Bin Zhang, Dapeng Li +3
Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning. However, almost all existing methods follow the principle of Indi…
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Bin Zhang, Dapeng Li +4
Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution. During centralized trainin…
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Dapeng Li, Bin Zhang +3
Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due…
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
Bin Zhang, Yunpeng Bai, Zhiwei Xu +2
The application of deep reinforcement learning in multi-agent systems introduces extra challenges. In a scenario with numerous agents, one of the most important concerns currently…
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
Yunpeng Bai, Chen Gong, Bin Zhang +3
Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an imp…
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Zhiwei Xu, Yunpeng Bai, Bin Zhang +2
Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods. Inspired by the intra-level and inter-level coordinati…
The -Divergence Reinforcement Learning Framework
Chen Gong, Qiang He, Yunpeng Bai +6
The framework of deep reinforcement learning (DRL) provides a powerful and widely applicable mathematical formalization for sequential decision-making. This paper present a novel D…
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…
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
Zhiwei Xu, Yunpeng Bai, Dapeng Li +2
As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results rece…
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…