Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach

Bin Zhang, Hangyu Mao, Jingqing Ruan, Ying Wen, Yang Li, Shao Zhang, Zhiwei Xu, Dapeng Li, Ziyue Li, Rui Zhao, Guoliang Fan, Lijuan Li

Keywords: Multi-Agent Systems, Large Language Model, Large-Scale Decision-Making

TL;DR: A novel and modular framework called LLaMAC that integrates internal and external feedback mechanisms is presented to enhance the collaborative performance of large-scale multi-agent systems based on Large Language Models.

Abstract: The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS). However, as the number of agents increases, the issues of hallucination in LLMs and coordination in MAS have become increasingly prominent. Additionally, the efficient utilization of tokens emerges as a critical consideration when employing LLMs to facilitate the interactions among a substantial number of agents. In this paper, we develop a modular framework called LLaMAC to mitigate these challenges. LLaMAC implements a value distribution encoding similar to that found in the human brain, utilizing internal and external feedback mechanisms to facilitate collaboration and iterative reasoning among its modules. Through evaluations involving system resource allocation and robot grid transportation, we demonstrate the considerable advantages afforded by our proposed approach.

Submission Number: 2