Can LLMs improve MARL agent training?
LLM-MEDIATED GUIDANCE OF MARL SYSTEMS
March 19, 2025
https://arxiv.org/pdf/2503.13553This research explores using Large Language Models (LLMs) to guide multi-agent reinforcement learning (MARL) systems toward desired behaviors. It tests two types of LLM-mediated interventions—"rule-based" and "natural language"—in a simulated aerial wildfire suppression environment. Key findings relevant to LLM-based multi-agent systems include: LLMs can significantly accelerate MARL training and improve agent coordination, particularly with early interventions; natural language interventions are more impactful than rule-based ones; and different LLMs exhibit distinct strengths in handling these interventions, suggesting a potential for combining their capabilities.