LLawCo: Learning Laws of Cooperation for Modeling Embodied Multi-Agent Behavior
Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years. However, existing large language model (LLM)-based agents often exhibit behaviors that are misaligned with their partners or inconsistent with the environment state, leading to inefficient cooperation and poor task success. To address this challenge, we propose a novel framework, Learning Laws of Cooperation (LLawCo), that enables embodied agents to autonomously align with both their partners and task objectives. Our framework allows agents to reflect on past failu
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- LinkedLinked via unknownagent-tools →
- LinkedLinked via unknownAgentic AI for Robot Teams →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “LLawCo: Learning Laws of Cooperation for Modeling Embodied M” ≈ “AgentCore-8B””
- LinkedLinked via unknownjaimasih05-commits/swarm-foraging-qlearn →
- PossiblePossibly related (embedding) · 46%baidu-baige/LoongForge →
- FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent →
“Fuzzy title match (0.94): “LLawCo: Learning Laws of Cooperation for Modeling Embodied M” ≈ “SWE-agent/SWE-agent””
- FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent →
“Fuzzy title match (0.94): “LLawCo: Learning Laws of Cooperation for Modeling Embodied M” ≈ “zhayujie/CowAgent””
- FuzzySimilar title/name (fuzzy) · 66%open-multi-agent/open-multi-agent →
“Fuzzy title match (0.78): “LLawCo: Learning Laws of Cooperation for Modeling Embodied M” ≈ “open-multi-agent/open-multi-agent””