REINFORCEMENT LEARNING TECHNIQUES FOR SELECTING A SOFTWARE POLICY NETWORK AND AUTONOMOUSLY CONTROLLING A CORRESPONDING SOFTWARE CLIENT BASED ON SELECTED POLICY NETWORK

• 1. 用于选择软件策略网络并且基于所选择的策略网络自主控制对应软件客户端的强化学习技术 [P] . 中国专利： CN113227973A . 2021-08-06
• 2. 一种基于策略的隧道选择方法及软件定义网络控制器 [P] . 中国专利： CN108989210B . 2021.08.24
• 3. REINFORCEMENT LEARNING TECHNIQUES FOR SELECTING A SOFTWARE POLICY NETWORK AND AUTONOMOUSLY CONTROLLING A CORRESPONDING SOFTWARE CLIENT BASED ON SELECTED POLICY NETWORK [P] . US2021334628A1 . 2021-10-28

  机译： 加强学习技术，用于选择软件策略网络，并基于所选策略网络自主控制相应的软件客户端

• 4. REINFORCEMENT LEARNING TECHNIQUES FOR SELECTING A SOFTWARE POLICY NETWORK AND AUTONOMOUSLY CONTROLLING A CORRESPONDING SOFTWARE CLIENT BASED ON SELECTED POLICY NETWORK [P] . EP3871088A1 . 2021-09-01

  机译： 加强学习技术，用于选择软件策略网络，并基于所选策略网络自主控制相应的软件客户端

• 5. Training action selection neural networks using off-policy actor critic reinforcement learning [P] . 美国专利： US10706352B2 . 2020-07-07

  机译： 使用非政策演员批评家强化学习来训练动作选择神经网络

• 1. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks [J] . Zhang Huibin, Wang Yuqiao, Chen Haoran, Optical fiber technology . 2017,第deca期
• 2. Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks [J] . Haonan Chang, Zhuo Xu, Masayoshi Tomizuka IFAC PapersOnLine . 2020,第2期
• 3. Policy-Based Management for Green Mobile Networks Through Software-Defined Networking [J] . Huertas Celdran Alberto, Gil Perez Manuel, Garcia Clemente Felix J., Mobile networks & applications . 2019,第2期
• 4. Mean First-Passage Time Control Policy versus Reinforcement-Learning Control Policy in Gene Regulatory Networks [C] . Golnaz Vahedi, Babak Faryabi, Jean-Francois Chamberland, American Control Conference . 2008
• 5. Reinforcement learning techniques for controlling resources in power networks. [D] . Kowli, Anupama Sunil. 2013
• 6. QoS improvement with an optimum controller selection for software-defined networks [O] . Jehad Ali, Byeong-hee Roh, Seungwoon Lee 2015
• 7. Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks [O] . Haonan Chang, Zhuo Xu, Masayoshi Tomizuka 2020
• 8. Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System [R] . Williams, Peggy S. 2004

• 1. Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT [J] . Wugedele Bao ,Celimuge Wu ,Siri Guleng . 中国通信 . 2021,第006期
• 2. 如何实现网络化治理?——评Network Governance and the Differentiated Policy:Selected Essays,Volume I与Interactive Governance:Advancing the Paradigm [J] . 郑鑫 ,毛寿龙 . 公共行政评论 . 2019,第3期
• 3. Blockchain-Based Secure Distributed Control for Software Defined Optical Networking [J] . Hui Yang ,Yongshen Liang ,Qiuyan Yao . 中国通信 . 2019,第006期
• 4. Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks [J] . 王妮炜 ,费泽松 ,邢成文 . 北京理工大学学报：英文版 . 2015,第2期
• 5. Positive software fault-tolerate technique based on time policy [J] . SONG Yu ,CHEN Qi ,HE Xin . 通讯和计算机 . 2007,第008期
• 6. 一种基于客户端的网络服务性能测试模型建立策略 [C] . 郐言 ,毛晓光 ,陈五一 . 中国科学院计算技术研究所第八届计算机科学与技术研究生学术讨论会 . 2004
• 7. 基于软件定义网络的等价多路由网络负载均衡策略 [A] . 栾泽宇 . 2017