Neural Collective Behavior Modeling and Reinforcement Learning

Abstract

This paper explores the application of mathematical modeling and reinforcement learning to the study of neural network collective behavior. Traditional neural network optimization often relies on manual parameter tuning, limiting the model's adaptability. We propose a novel framework that integrates these approaches, simulating the collective learning process of neural networks using a mathematical model. This model leverages reinforcement learning to automatically optimize the network's parameters and enhance its generalization capabilities. The core mechanism involves mapping the neural network's collective behavior to a reinforcement learning environment, allowing the agent to iteratively improve the network's performance through trial and error. This approach offers a powerful and automated method for training neural networks, potentially surpassing traditional optimization techniques. The research demonstrates the effectiveness of this combined approach in improving the robustness and generalization of neural network models.