Closed-Form Congestion Control Policies Learned via Deep Symbolic Regression for Fronthaul Networks

This paper proposes a methodology to learn closed-form mathematical expressions (symbolic policies) that approximate the behavior of a baseline reinforcement learning congestion control policy, while maintaining its performance and generalization capabilities. The symbolic policies overcome the challenges of neural network models regarding real-time inference and interpretability.