  Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical
network optimization. However, the flexibility and efficiency of current
DRL-based solutions for optical network optimization require further
improvement. Currently, generative models have showcased their significant
performance advantages across various domains. In this paper, we introduce
OpticGAI, the AI-generated policy design paradigm for optical networks. In
detail, it is implemented as a novel DRL framework that utilizes generative
models to learn the optimal policy network. Furthermore, we assess the
performance of OpticGAI on two NP-hard optical network problems, Routing and
Wavelength Assignment (RWA) and dynamic Routing, Modulation, and Spectrum
Allocation (RMSA), to show the feasibility of the AI-generated policy paradigm.
Simulation results have shown that OpticGAI achieves the highest reward and the
lowest blocking rate of both RWA and RMSA problems. OpticGAI poses a promising
direction for future research on generative AI-enhanced flexible optical
network optimization.
