Comparative Analysis of Reinforcement Learning Architectures in Multi-Agent Environments for Coordinated Problem Solving

Abstract

Reinforcement Learning (RL) has seen significant advancements in multi-agent environments, particularly for coordinated problem-solving tasks. This study provides a comparative analysis of key RL architectures, including centralized, decentralized, and hybrid frameworks, examining their effectiveness in scenarios requiring cooperation, competition, or mixed behaviors among agents. We evaluate these architectures across various metrics, including scalability, learning efficiency, and adaptability, highlighting trade-offs in their design and implementation. Additionally, the role of communication protocols, reward mechanisms, and policy-sharing strategies are explored to understand their influence on system performance. This analysis serves as a foundation for optimizing RL models in multi-agent systems, providing insights into their applicability across domains such as robotics, traffic management, and distributed computing.