LIET combines a finetuned local utility function with an iteratively updated shared knowledge list to improve multi-agent LLM planning, and it outperforms CoELA, ProAgent, and RoCo on C-WAH and TDW-MAT benchmarks.
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Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
LIET combines a finetuned local utility function with an iteratively updated shared knowledge list to improve multi-agent LLM planning, and it outperforms CoELA, ProAgent, and RoCo on C-WAH and TDW-MAT benchmarks.