13 citations · 13 across the 3 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…