13 citations · 35 across the 27 of their papers we have counts for
28 papers
QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning
Yuanjun Li, Bin Zhang, Hao Chen +3
Value decomposition (VD) methods have achieved remarkable success in cooperative multi-agent reinforcement learning (MARL). However, their reliance on the max operator for temporal…
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…
Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks
Wentao Deng, Jiahuan Pei, Zhiwei Xu +3
A multi-agent system (MAS) enhances its capacity to solve complex natural language processing (NLP) tasks through collaboration among multiple agents, where consensus-seeking serve…
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems
Minghang Zhu, Zhengliang Shi, Zhiwei Xu +5
The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, su…
Graph of Verification: Structured Verification of LLM Reasoning with Directed Acyclic Graphs
Jiwei Fang, Bin Zhang, Changwei Wang +2
Verifying the complex and multi-step reasoning of Large Language Models (LLMs) is a critical challenge, as holistic methods often overlook localized flaws. Step-by-step validation…
NGENT: Next-Generation AI Agents Must Integrate Multi-Domain Abilities to Achieve Artificial General Intelligence
Zhicong Li, Hangyu Mao, Jiangjin Yin +4
This paper argues that the next generation of AI agent (NGENT) should integrate across-domain abilities to advance toward Artificial General Intelligence (AGI). Although current AI…
Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition
Changwei Wang, Shunpeng Chen, Yukun Song +11
Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local re…
QLLM: Do We Really Need a Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning?
Yuanjun Li, Zhouyang Jiang, Bin Zhang +3
Credit assignment remains a fundamental challenge in multi agent reinforcement learning (MARL) and is commonly addressed through value decomposition under the centralized training…
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…
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…
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Ziyue Li +7
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work studies the former. Specifically, the Perception and Deci…
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…
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
Jingqing Ruan, Yihong Chen, Bin Zhang +8
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the…
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…