LLM-Driven Multi-Agent Decision Systems with Reinforcement Learning-Based Adaptive Communication
DOI:
https://doi.org/10.66238/fsrma54Keywords:
LLM agents, Adaptive communication, Reinforcement learning, Multi-agent systems, Decision optimizationAbstract
Effective communication among LLM-driven agents is critical for achieving high-quality collaborative decision-making. This study proposes an adaptive communication framework that leverages reinforcement learning to dynamically adjust message frequency, content granularity and routing strategies among agents. The system is trained on a multi-domain dataset comprising 9,800 tasks across negotiation, planning and data reasoning scenarios. A communication-aware reward function is designed to balance performance gains and communication cost. Experimental results demonstrate a 25.6% improvement in task success rate and a 30.2% reduction in communication overhead compared to fixed communication protocols. The proposed approach highlights the importance of adaptive communication policies in large-scale agent systems..
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