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20242026
most citedFrom Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training

1 citations · 1 across the 5 of their papers we have counts for

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cs.MA2026

Group Perspective Matters: Regulating Debate Relationships Can Mitigate Blind Conformity in Multi-Agent Debate

Hao Wu, Shoucheng Song, Chang Yao +4

Multi-Agent Debate (MAD) improves the reasoning performance of Large Language Models (LLMs) through multi-round interaction. However, LLMs in MAD are highly susceptible to blind co…

cs.MA2025

Think How Your Teammates Think: Active Inference Can Benefit Decentralized Execution

Hao Wu, Shoucheng Song, Chang Yao +4

In multi-agent systems, explicit cognition of teammates' decision logic serves as a critical factor in facilitating coordination. Communication (i.e., ``\textit{Tell}'') can assist…

cs.MA2025

From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination

Chang Yao, Youfang Lin, Shoucheng Song +4

Continual Multi-Agent Reinforcement Learning (Co-MARL) requires agents to address catastrophic forgetting issues while learning new coordination policies with the dynamics team. In…

cs.MA2025

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness

Shoucheng Song, Youfang Lin, Sheng Han +4

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challengi…

cs.MA2024

Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space

Xiaoyang Yu, Youfang Lin, Shuo Wang +2

In a multi-agent system (MAS), action semantics indicates the different influences of agents' actions toward other entities, and can be used to divide agents into groups in a physi…

cs.MA2024

GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning

Xiaoyang Yu, Youfang Lin, Xiangsen Wang +2

Previous deep multi-agent reinforcement learning (MARL) algorithms have achieved impressive results, typically in homogeneous scenarios. However, heterogeneous scenarios are also v…