Collective Emergence in Multi-Agent Reinforcement Learning

Abstract

This paper investigates the application of collective emergence theory to multi-agent reinforcement learning (MARL). The core claim is that leveraging principles of collective emergence – specifically localized rules and nonlinear interactions – can guide multi-agent systems to spontaneously generate novel behaviors and problem-solving capabilities within complex environments. Traditional MARL often focuses on optimizing individual agent performance, potentially limiting the system's overall intelligence. This work proposes a framework where the environment and reward functions are designed to explicitly encourage emergent group behavior. The key mechanism involves translating the theoretical concepts of "local rules" and "nonlinear interactions" into practical design choices for MARL. We explore how these interactions can lead to emergent coordination and innovative solutions, representing a shift towards understanding and harnessing the power of collective intelligence in reinforcement learning. The results, though theoretical in this initial presentation, demonstrate a potential pathway for developing more robust and adaptive multi-agent systems.