OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
This work addresses optimization challenges in optical networks, offering a novel AI-generated policy paradigm that is domain-specific and incremental in its approach.
The paper tackles the problem of improving flexibility and efficiency in optical network optimization by introducing OpticGAI, a DRL framework that uses generative models to learn optimal policies, achieving the highest reward and lowest blocking rate for RWA and RMSA problems.
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.