13 citations · 52 across the 27 of their papers we have counts for
28 papers
BiFE: Search-Efficient Discovery of CPU-Only Branching Policies via LLM-based Bi-Fidelity Evolution
Ce Zhang, Bin Zhang, Zhiwei Xu +5
In branch-and-bound (B&B) for mixed-integer linear programming (MILP), branching variable selection critically impacts efficiency. Existing neural branching policies often require…
AirAnchor: Bridging Local and Global Spatial Information for Zero-Shot Aerial Vision-and-Language Navigation
Shanwei Fan, Bin Zhang, Zhiwei Xu +4
Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accurate navigation relies on both lo…
CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation
Gengxin Sun, Ruihao Yu, Liangyi Yin +3
Ensuring robust and fair interview assessment remains a key challenge in AI-driven evaluation. This paper presents CoMAI, a general-purpose multi-agent interview framework designed…
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…
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…
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency
Zhishuai Li, Xiang Wang, Jingjing Zhao +8
Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face c…
SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models
Bin Zhang, Yuxiao Ye, Guoqing Du +8
Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods.Nevertheless, as a nascent research…
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…
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…
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Yilun Kong, Jingqing Ruan, Yihong Chen +9
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend…
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…
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…
PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Yiqun Chen, Hangyu Mao, Jiaxin Mao +5
Centralized Training with Decentralized Execution (CTDE) has emerged as a widely adopted paradigm in multi-agent reinforcement learning, emphasizing the utilization of global infor…
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…