The Importance of Information Sharing in Multi-Agent Systems: Addressing Reinforcement Learning Challenges

Many multi-agent systems (MASs) involve both cooperation and competition among agents. While some of these systems achieve near-optimal outcomes, others result in suboptimal performance and efficiency. The primary objective is to identify strategies that optimize individual agent performance while enhancing overall system efficiency. This paper examines the conditions that contribute to suboptimal outcomes and explores a potential solution to improve system performance. The findings suggest that limited information sharing can enhance overall effectiveness. This study introduces a generalized model of networked agents, where each agent learns and optimizes its actions while being influenced by the decisions of others through a reward-based mechanism. The simulation results demonstrate that a combination of coordinated competition and information sharing can lead to improved outcomes and increased rewards for all agents involved.