DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS

• 1. 利用快速更新循环神经网络和慢速更新循环神经网络的深度强化学习 [P] . 中国专利： CN112119406A . 2020-12-22
• 2. 使用递归神经网络进行血管疾病检测的方法和系统 [P] . 中国专利： CN107545269B . 2021.06.08
• 3. DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS [P] . US2021097373A1 . 2021-04-01

  机译： 深度加强学习，快速更新经常性神经网络和缓慢更新经常性神经网络

• 4. DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS [P] . 美国专利： US2019370637A1 . 2019-12-05

  机译： 通过快速更新递归神经网络和缓慢更新递归神经网络进行深度强化学习

• 5. Method of estimating flow rate and of detecting leak of wide area water using recurrent analysis recurrent neural network and deep neural network [P] . 韩国专利： KR20190094692A . 2019-08-14

  机译： 递归分析递归神经网络和深度神经网络估计流量和检测广域水泄漏的方法

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• 3. Remaining Useful Life Prediction for a Roller in a Hot Strip Mill Based on Deep Recurrent Neural Networks [J] . Ruihua Jiao, Kaixiang Peng, Jie Dong 自动化学报（英文版） . 2021,第007期
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• 8. R3L: Connecting Deep Reinforcement Learning To Recurrent Neural Networks For Image Denoising Via Residual Recovery [C] . Rongkai Zhang, Jiang Zhu, Zhiyuan Zha, IEEE International Conference on Image Processing . 2021
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