[1] Nath P K, Venkatesh T. Lightpath routing and wavelength assignment for static demand in translucent optical networks [J]. Photonic Network Communications, 2020, 39(7): 103-119.
[2] Yang Xiuqing, Chen Haiyan. Application of optical communication technique in the internet of things [J]. Chinese Optics, 2014, 7(6): 889-896. (in Chinese)
[3] Li Haitao. Technical approach analysis and development prospects of optical communication technology in China deep space TT&C network [J]. Infrared and Laser Engineering, 2020, 49(5): 20201003. (in Chinese) doi:  10.3788/IRLA20201003
[4] Yang Junbo, Yang Jiankun, Li Xiujian, et al. Choice and control of routes in crossover optical interconnection network [J]. Optics and Precision Engineering, 2010, 18(6): 1249-1257. (in Chinese)
[5] Sun Zhaowei, Liu Xuekui, Wu Xiande, et al. Path planning based on ant colony and genetic fusion algorithm for communication supporting spacecraft [J]. Optics and Precision Engineering, 2013, 21(12): 3308-3316. (in Chinese) doi:  10.3788/OPE.20132112.3308
[6] Guo Xiuzhen, Hou Lixin, Yin Zhaotai, et al. All-optical routing control based on coherently induced high reflection band and high transmission band in a medium of cold atoms [J]. Chinese Optics, 2011, 4(4): 355-362. (in Chinese)
[7] Zhang Min, Xu Bo, Cai Yi, et al. Routing and wavelength assignment based on genetic algorithm in large scale WDM network [J]. Optical Communication Technology, 2018, 42(11): 1-4. (in Chinese)
[8] Wang Weilong, Li Yongjun, Zhao Shanghong, et al. Routing and wavelength assignment based on load balance for optical satellite network [J]. Laser & Optoelectronics Progress, 2021, 58(7): 0706004. (in Chinese)
[9] Shi Xiaodong, Li Yongjun, Zhao Shanghong, et al. Ant colony optimization routing and wavelength technology for software-defined satellite optical networks [J]. Infrared and Laser Engineering, 2021, 51(7): 20200125. (in Chinese) doi:  10.3788/IRLA20200125
[10] Martín I, Troia S, Hernández J A, et al. Machine learning based routing -and wavelength assignment in software-defined optical networks [J]. IEEE Transactions on Network and Service Management, 2019, 16(3): 871-883. doi:  10.1109/TNSM.2019.2927867
[11] Mnih V, Kavukcuoglu K, Silver D, et al. Human level control through deep reinforcement learning. [J]. Nature, 2015, 518(7540): 529-533. doi:  10.1038/nature14236
[12] Li Zhongtao. Wireless communication node coverage optimization based double deep Q-learning [J]. Electronic Technology & Software Engineering, 2021(14): 1-3. (in Chinese)
[13] Rao Ning, Xu Hua, Qi Zisen, et al. Communication interference resource allocation method of deep reinforcement learning based on maximum policy entropy [J]. Journal of Northwestern Polytechnical University, 2021, 39(5): 1077-1086. (in Chinese) doi:  10.1051/jnwpu/20213951077
[14] Zhao Zipiao, Zhao Yongli, Ma Haoli, et al. Cost-efficient routing, modulation, wavelength and port assignment using reinforcement learning in optical transport networks [J]. Optical Fiber Technology, 2021, 64: 102571.
[15] Li Xin, Zhao Yongli, Li Yajie, et al. Multi-objective routing and resource allocation based on reinforcement learning in optical transport networks[C]//2020 Asia Communications and Photonics Conference (ACP) and International Conference on Information Photonics and Optical Communications (IPOC), 2020: 1-3.
[16] Chen Xiaoliang, Li Baojia, Proietti Roberto, et al. DeepRMSA: A deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks [J]. Journal of Lightwave Technology, 2019, 37(16): 4155-4163. doi:  10.1109/JLT.2019.2923615