OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization

TL;DR

OpticGAI introduces a novel DRL framework utilizing generative models to optimize optical networks, demonstrating superior performance on complex NP-hard problems like RWA and RMSA.

Contribution

It presents the first AI-generated policy design paradigm for optical networks using generative models within a DRL framework.

Findings

Achieves highest reward in RWA and RMSA problems.

Lowest blocking rate compared to existing methods.

Demonstrates feasibility of generative AI in optical network optimization.

Abstract

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.