Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models

TL;DR

This paper introduces a framework combining large language models with strategic reasoning and real-time adaptation for multi-agent environments, significantly improving collaboration and decision-making in dynamic, noisy scenarios.

Contribution

It presents a novel approach integrating game-theoretic principles with real-time feedback to enhance LLM-driven multi-agent coordination and adaptability.

Findings

Achieves up to 26% improvement over PPO baselines in noisy environments.

Maintains real-time latency under 1.05 milliseconds.

Enhances collaboration efficiency and task completion in dynamic settings.

Abstract

Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic, real-time, multi-agent scenarios, such as collaborative environments in which agents continuously adapt to each other's behavior, as in cooperative gameplay settings. In this paper, we bridge this gap by combining LLM-driven agents with strategic reasoning and real-time adaptation in cooperative, multi-agent environments grounded in game-theoretic principles such as belief consistency and Nash equilibrium. The proposed framework applies broadly to dynamic scenarios in which agents coordinate, communicate, and make decisions in response to continuously changing conditions. We provide real-time strategy refinement and adaptive feedback mechanisms that enable agents to dynamically adjust policies based on immediate contextual interactions, in contrast to previous efforts that evaluate LLM capabilities in static or turn-based settings. Empirical results show that our method achieves up to a 26\% improvement in return over PPO baselines in high-noise environments, while maintaining real-time latency under 1.05 milliseconds. Our approach improves collaboration efficiency, task completion rates, and flexibility, illustrating that game-theoretic guidance integrated with real-time feedback enhances LLM performance, ultimately fostering more resilient and flexible strategic multi-agent systems.