OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
<|reference_start|>OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization: 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.<|reference_end|>
@article{li2024opticgai:,
    title={OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical
  Networks Optimization},
    author={Siyuan Li, Xi Lin, Yaju Liu, Gaolei Li, Jianhua Li},
    journal={arXiv preprint arXiv:2406.15906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15906},
    primaryClass={cs.NI cs.AI}
}