基于多智能体协作的复杂系统模拟

Abstract

This paper presents a novel approach to simulating complex systems by leveraging the power of multi-agent collaboration. The core idea is to construct a population of intelligent agents, each with varying levels of cognitive ability, and task them with solving complex problems collectively. Reinforcement learning is employed to train these agents, enabling them to learn optimal collaborative strategies. The system's emergent behavior is then utilized for both prediction and control purposes. Unlike traditional single-agent simulation methods, this approach capitalizes on the distributed intelligence and information exchange among agents, leading to potentially more accurate and efficient system modeling. We demonstrate the feasibility and effectiveness of this methodology through a theoretical framework, outlining the key components and operational principles. The primary contribution lies in the systematic integration of multi-agent reinforcement learning with complex system simulation, offering a promising avenue for tackling systems exhibiting emergent behaviors and intricate interactions.