Deep Reinforcement Learning-Based Optimization of Identical-Dual-Band Filters

Designing identical dual-band optical filters remains a complex optimization challenge in photonics and optical communication systems. Conventional methods, which rely on iterative electromagnetic simulations or analytical approximations, often suffer from limited generalizability and high computational costs. In this work, we propose a deep reinforcement learning (RL) framework for the autonomous optimization of identical dual-band fiber Bragg grating (FBG) filters. A policy network based on a three-layer fully connected neural architecture is trained using a proximal policy optimization algorithm to minimize the full width at half maximum (FWHM) of both transmission bands while maintaining spectral symmetry and identical channel characteristics. The deep RL-based design achieves a 43% reduction in FWHM and a 49% reduction in grating length compared to baseline designs, without sacrificing reflectivity or channel uniformity. This study demonstrates the feasibility and effectiveness of deep RL as a powerful optimization tool for complex photonic systems, providing a scalable and data-efficient pathway toward next-generation optical device design.