OpticGAI：面向光网络优化的生成式AI辅助深度强化学习
OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
AI总结：
针对现有深度强化学习光网络优化方案灵活性与效率不足的问题，提出利用生成模型学习最优策略网络的DRL框架OpticGAI，在RWA和RMSA两个NP难问题上取得最高奖励和最低阻塞率。
摘要：
深度强化学习（DRL）被认为是光网络优化的一种极具前景的工具。然而，当前基于DRL的光网络优化方案的灵活性和效率仍需进一步提升。目前，生成式模型已在多个领域展现出显著的性能优势。本文提出了OpticGAI，一种面向光网络的AI生成策略设计范式。具体而言，它被实现为一个新型DRL框架，利用生成式模型来学习最优策略网络。此外，我们在两个NP难的光网络问题——路由与波长分配（Routing and Wavelength Assignment, RWA）以及动态路由、调制与频谱分配（dynamic Routing, Modulation, and Spectrum Allocation, RMSA）上评估了OpticGAI的性能，以验证AI生成策略范式的可行性。仿真结果表明，OpticGAI在RWA和RMSA问题上均实现了最高的奖励值和最低的阻塞率。OpticGAI为未来生成式AI增强的灵活光网络优化研究提供了一个颇具前景的方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
