Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
This addresses the problem of improving multi-agent cooperation and planning in embodied AI for researchers and practitioners, though it appears incremental as it builds on existing paradigms like centralized training with decentralized execution.
The paper tackles the problem of weak adaptation capabilities in LLM-based planning algorithms for multi-agent embodied environments by introducing the LIET framework, which enables agents to learn individually and evolve as a team, resulting in outperforming existing baselines on benchmarks like Communicative Watch-And-Help and ThreeD-World Multi-Agent Transport.
Large language models (LLMs) possess extensive knowledge bases and strong reasoning capabilities, making them promising tools for complex, multi-agent planning in embodied environments. However, despite LLMs' advanced abilities and the sophisticated modular design of agentic methods, existing LLM-based planning algorithms remain limited by weak adaptation capabilities to multi-agent embodied scenarios. We address this limitation by introducing a framework that enables LLM agents to learn and evolve both before and during test time, equipping them with environment-relevant knowledge for better planning and enhanced communication for improved cooperation. Inspired by centralized training with decentralized execution in multi-agent reinforcement learning, we propose a \textit{Learn as Individuals, Evolve as a Team (LIET)} paradigm for multi-agent LLMs adaptation. At the individual level, LLM agents learn a local utility function from exploratory datasets to better comprehend the embodied environment, which is then queried during test time to support informed decision-making. At the team level, LLM agents collaboratively and iteratively maintain and update a shared cooperation knowledge list based on new experiences, using it to guide more effective communication. By combining individual learning with team evolution, LIET enables comprehensive and flexible adaptation for LLM agents. Our experiments on Communicative Watch-And-Help and ThreeD-World Multi-Agent Transport benchmarks demonstrate that LIET, instantiated with both LLaMA and GPT-4o, outperforms existing baselines and exhibits strong cooperative planning abilities.