Abstract: In recent years, significant research attention has been directed towards swarm intelligence. The Milling behavior of fish schools, a prime example of swarm intelligence, shows how simple rules followed by individual agents lead to complex collective behaviors. This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior, over-coming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments. Based on this foundation, a novel Graph Convolutional Networks (GCN)-Critic MAD-DPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system.  Simulation experiments demonstrate that, compared to traditional single-agent algorithms, the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and morenaturally aligned Milling behavior. Additionally, a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed, providing a robust tool for exploring dynamic behavioral changes under various conditions.

Key words: Collective motion')">Collective motion, Collective behavior, Self-organization, Fish school, Multi-agent reinforcement learning