Статті в журналах з теми "Multi-Objective Reinforcement Learning"
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Horie, Naoto, Tohgoroh Matsui, Koichi Moriyama, Atsuko Mutoh, and Nobuhiro Inuzuka. "Multi-objective safe reinforcement learning: the relationship between multi-objective reinforcement learning and safe reinforcement learning." Artificial Life and Robotics 24, no. 3 (February 8, 2019): 352–59. http://dx.doi.org/10.1007/s10015-019-00523-3.
Повний текст джерелаKim, Man-Je, Hyunsoo Park, and Chang Wook Ahn. "Nondominated Policy-Guided Learning in Multi-Objective Reinforcement Learning." Electronics 11, no. 7 (March 28, 2022): 1069. http://dx.doi.org/10.3390/electronics11071069.
Повний текст джерелаDrugan, Madalina, Marco Wiering, Peter Vamplew, and Madhu Chetty. "Special issue on multi-objective reinforcement learning." Neurocomputing 263 (November 2017): 1–2. http://dx.doi.org/10.1016/j.neucom.2017.06.020.
Повний текст джерелаPerez, Julien, Cécile Germain-Renaud, Balazs Kégl, and Charles Loomis. "Multi-objective Reinforcement Learning for Responsive Grids." Journal of Grid Computing 8, no. 3 (June 8, 2010): 473–92. http://dx.doi.org/10.1007/s10723-010-9161-0.
Повний текст джерелаNguyen, Thanh Thi, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, and Chee Peng Lim. "A multi-objective deep reinforcement learning framework." Engineering Applications of Artificial Intelligence 96 (November 2020): 103915. http://dx.doi.org/10.1016/j.engappai.2020.103915.
Повний текст джерелаGarcía, Javier, Rubén Majadas, and Fernando Fernández. "Learning adversarial attack policies through multi-objective reinforcement learning." Engineering Applications of Artificial Intelligence 96 (November 2020): 104021. http://dx.doi.org/10.1016/j.engappai.2020.104021.
Повний текст джерелаYamamoto, Hiroyuki, Tomohiro Hayashida, Ichiro Nishizaki, and Shinya Sekizaki. "Hypervolume-Based Multi-Objective Reinforcement Learning: Interactive Approach." Advances in Science, Technology and Engineering Systems Journal 4, no. 1 (2019): 93–100. http://dx.doi.org/10.25046/aj040110.
Повний текст джерелаGarcía, Javier, Roberto Iglesias, Miguel A. Rodríguez, and Carlos V. Regueiro. "Incremental reinforcement learning for multi-objective robotic tasks." Knowledge and Information Systems 51, no. 3 (September 22, 2016): 911–40. http://dx.doi.org/10.1007/s10115-016-0992-2.
Повний текст джерелаSchneider, Stefan, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. "Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning." IEEE Transactions on Network and Service Management 18, no. 3 (September 2021): 3829–42. http://dx.doi.org/10.1109/tnsm.2021.3076503.
Повний текст джерелаFerreira, Leonardo Anjoletto, Carlos Henrique Costa Ribeiro, and Reinaldo Augusto da Costa Bianchi. "Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems." Applied Intelligence 41, no. 2 (May 1, 2014): 551–62. http://dx.doi.org/10.1007/s10489-014-0534-0.
Повний текст джерелаParisi, Simone, Matteo Pirotta, and Marcello Restelli. "Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation." Journal of Artificial Intelligence Research 57 (October 21, 2016): 187–227. http://dx.doi.org/10.1613/jair.4961.
Повний текст джерела陶, 海成, 湛. 卜, and 杰. 曹. "A multi-objective reinforcement learning framework for community deception." SCIENTIA SINICA Informationis 51, no. 7 (July 1, 2021): 1131. http://dx.doi.org/10.1360/ssi-2020-0229.
Повний текст джерелаKOBAYASHI, Taisuke. "Multi-Objective Switchable Reinforcement Learning by using Reservoir Computing." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P1—H03. http://dx.doi.org/10.1299/jsmermd.2017.2p1-h03.
Повний текст джерелаWestbrink, Fabian, Alexander Elbel, Andreas Schwung, and Steven X. Ding. "Optimization of DEM parameters using multi-objective reinforcement learning." Powder Technology 379 (February 2021): 602–16. http://dx.doi.org/10.1016/j.powtec.2020.10.067.
Повний текст джерелаRuiz-Montiel, Manuela, Lawrence Mandow, and José-Luis Pérez-de-la-Cruz. "A temporal difference method for multi-objective reinforcement learning." Neurocomputing 263 (November 2017): 15–25. http://dx.doi.org/10.1016/j.neucom.2016.10.100.
Повний текст джерелаZou, Fei, Gary G. Yen, Lixin Tang, and Chunfeng Wang. "A reinforcement learning approach for dynamic multi-objective optimization." Information Sciences 546 (February 2021): 815–34. http://dx.doi.org/10.1016/j.ins.2020.08.101.
Повний текст джерелаQin, Yao, Hua Wang, Shanwen Yi, Xiaole Li, and Linbo Zhai. "Virtual machine placement based on multi-objective reinforcement learning." Applied Intelligence 50, no. 8 (March 6, 2020): 2370–83. http://dx.doi.org/10.1007/s10489-020-01633-3.
Повний текст джерелаComsa, Ioan Sorin, Mehmet Aydin, Sijing Zhang, Pierre Kuonen, and Jean–Frédéric Wagen. "Multi Objective Resource Scheduling in LTE Networks Using Reinforcement Learning." International Journal of Distributed Systems and Technologies 3, no. 2 (April 2012): 39–57. http://dx.doi.org/10.4018/jdst.2012040103.
Повний текст джерелаLi, Dazi, Fuqiang Zhu, Xiao Wang, and Qibing Jin. "Multi-objective reinforcement learning for fed-batch fermentation process control." Journal of Process Control 115 (July 2022): 89–99. http://dx.doi.org/10.1016/j.jprocont.2022.05.003.
Повний текст джерелаShresthamali, Shaswot, Masaaki Kondo, and Hiroshi Nakamura. "Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement Learning." Journal of Low Power Electronics and Applications 12, no. 4 (October 8, 2022): 53. http://dx.doi.org/10.3390/jlpea12040053.
Повний текст джерелаChen, SenPeng, Jia Wu, and XiYuan Liu. "EMORL: Effective multi-objective reinforcement learning method for hyperparameter optimization." Engineering Applications of Artificial Intelligence 104 (September 2021): 104315. http://dx.doi.org/10.1016/j.engappai.2021.104315.
Повний текст джерелаBi, Yu, Carlos Colman Meixner, Monchai Bunyakitanon, Xenofon Vasilakos, Reza Nejabati, and Dimitra Simeonidou. "Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement." IEEE Transactions on Network and Service Management 18, no. 4 (December 2021): 4134–50. http://dx.doi.org/10.1109/tnsm.2021.3127685.
Повний текст джерелаLepenioti, Katerina, Alexandros Bousdekis, Dimitris Apostolou, and Gregoris Mentzas. "Human-Augmented Prescriptive Analytics With Interactive Multi-Objective Reinforcement Learning." IEEE Access 9 (2021): 100677–93. http://dx.doi.org/10.1109/access.2021.3096662.
Повний текст джерелаLi, Qinyu, Longyu Yang, Pengjie Tang, and Hanli Wang. "Enhancing semantics with multi‐objective reinforcement learning for video description." Electronics Letters 57, no. 25 (October 8, 2021): 977–79. http://dx.doi.org/10.1049/ell2.12334.
Повний текст джерелаMannion, Patrick, Sam Devlin, Karl Mason, Jim Duggan, and Enda Howley. "Policy invariance under reward transformations for multi-objective reinforcement learning." Neurocomputing 263 (November 2017): 60–73. http://dx.doi.org/10.1016/j.neucom.2017.05.090.
Повний текст джерелаHuo, Lin, and Yuepeng Tang. "Multi-Objective Deep Reinforcement Learning for Personalized Dose Optimization Based on Multi-Indicator Experience Replay." Applied Sciences 13, no. 1 (December 27, 2022): 325. http://dx.doi.org/10.3390/app13010325.
Повний текст джерелаZhang, Kai, Sterling McLeod, Minwoo Lee, and Jing Xiao. "Continuous reinforcement learning to adapt multi-objective optimization online for robot motion." International Journal of Advanced Robotic Systems 17, no. 2 (March 1, 2020): 172988142091149. http://dx.doi.org/10.1177/1729881420911491.
Повний текст джерелаWang, Yuandou, Hang Liu, Wanbo Zheng, Yunni Xia, Yawen Li, Peng Chen, Kunyin Guo, and Hong Xie. "Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning." IEEE Access 7 (2019): 39974–82. http://dx.doi.org/10.1109/access.2019.2902846.
Повний текст джерелаYAMADA, Kazuaki. "Acquiring Conflict Avoidance Behaviors with Multi-Objective Reinforcement Learning in Multi-Agent Systems." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2P2—F15. http://dx.doi.org/10.1299/jsmermd.2018.2p2-f15.
Повний текст джерелаAbdelfattah, Sherif, Kathryn Kasmarik, and Jiankun Hu. "A robust policy bootstrapping algorithm for multi-objective reinforcement learning in non-stationary environments." Adaptive Behavior 28, no. 4 (August 15, 2019): 273–92. http://dx.doi.org/10.1177/1059712319869313.
Повний текст джерелаGarcía, Javier, Roberto Iglesias, Miguel A. Rodríguez, and Carlos V. Regueiro. "Directed Exploration in Black-Box Optimization for Multi-Objective Reinforcement Learning." International Journal of Information Technology & Decision Making 18, no. 03 (May 2019): 1045–82. http://dx.doi.org/10.1142/s0219622019500093.
Повний текст джерелаWang, Hao, Zhongli Wang, and Xin Cui. "Multi-objective Optimization Based Deep Reinforcement Learning for Autonomous Driving Policy." Journal of Physics: Conference Series 1861, no. 1 (March 1, 2021): 012097. http://dx.doi.org/10.1088/1742-6596/1861/1/012097.
Повний текст джерелаBeeks, Martijn, Reza Refaei Afshar, Yingqian Zhang, Remco Dijkman, Claudy Van Dorst, and Stijn De Looijer. "Deep Reinforcement Learning for a Multi-Objective Online Order Batching Problem." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 435–43. http://dx.doi.org/10.1609/icaps.v32i1.19829.
Повний текст джерелаQin, Sheng, Shuyue Wang, Liyue Wang, Cong Wang, Gang Sun, and Yongjian Zhong. "Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning." Applied Sciences 11, no. 1 (December 24, 2020): 106. http://dx.doi.org/10.3390/app11010106.
Повний текст джерелаStudley, Matthew, and Larry Bull. "Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems." Artificial Life 13, no. 1 (January 2007): 69–86. http://dx.doi.org/10.1162/artl.2007.13.1.69.
Повний текст джерелаMa, Lianbo, Shi Cheng, Xingwei Wang, Min Huang, Hai Shen, Xiaoxian He, and Yuhui Shi. "Cooperative two-engine multi-objective bee foraging algorithm with reinforcement learning." Knowledge-Based Systems 133 (October 2017): 278–93. http://dx.doi.org/10.1016/j.knosys.2017.07.024.
Повний текст джерелаYliniemi, Logan, and Kagan Tumer. "Multi-objective multiagent credit assignment in reinforcement learning and NSGA-II." Soft Computing 20, no. 10 (March 28, 2016): 3869–87. http://dx.doi.org/10.1007/s00500-016-2124-z.
Повний текст джерелаLim, Cheolsun, and Myungsun Kim. "NAS based on Reinforcement Learning with Improved Multi-objective Reward Function." Journal of the Institute of Electronics and Information Engineers 59, no. 11 (November 30, 2022): 39–45. http://dx.doi.org/10.5573/ieie.2022.59.11.39.
Повний текст джерелаSong, Fuhong, Huanlai Xing, Xinhan Wang, Shouxi Luo, Penglin Dai, and Ke Li. "Offloading dependent tasks in multi-access edge computing: A multi-objective reinforcement learning approach." Future Generation Computer Systems 128 (March 2022): 333–48. http://dx.doi.org/10.1016/j.future.2021.10.013.
Повний текст джерелаJyothi, Rangappa, and Gorappa Ningappa Krishnamurthy. "Deep-Reinforcement Learning-Based Architecture for Multi-Objective Optimization of Stock Prediction." European Journal of Electrical Engineering and Computer Science 6, no. 4 (July 31, 2022): 9–16. http://dx.doi.org/10.24018/ejece.2022.6.4.436.
Повний текст джерелаHu, Can, Zhengwei Zhu, Lijia Wang, Chenyang Zhu, and Yanfei Yang. "An Improved Multi-Objective Deep Reinforcement Learning Algorithm Based on Envelope Update." Electronics 11, no. 16 (August 9, 2022): 2479. http://dx.doi.org/10.3390/electronics11162479.
Повний текст джерелаTao, Lue, Gongshu Wang, Yang Yang, Yun Dong, and Lijie Su. "Reinforcement Learning for Dynamic Mutation Process Control in Multi-Objective Differential Evolution." IFAC-PapersOnLine 55, no. 15 (2022): 117–22. http://dx.doi.org/10.1016/j.ifacol.2022.07.618.
Повний текст джерелаLuo, Shu, Linxuan Zhang, and Yushun Fan. "Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning." Computers & Industrial Engineering 159 (September 2021): 107489. http://dx.doi.org/10.1016/j.cie.2021.107489.
Повний текст джерелаM. Altaf, Meteb, Ahmed Samir Roshdy, and Hatoon S. AlSagri. "Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization." Computers, Materials & Continua 70, no. 2 (2022): 3955–67. http://dx.doi.org/10.32604/cmc.2022.019448.
Повний текст джерелаKozjek, Dominik, Andreja Malus, and Rok Vrabič. "Multi-objective adjustment of remaining useful life predictions based on reinforcement learning." Procedia CIRP 93 (2020): 425–30. http://dx.doi.org/10.1016/j.procir.2020.03.051.
Повний текст джерелаWang, Zheng, Tiansheng Zeng, Xuening Chu, and Deyi Xue. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade." Renewable Energy 203 (February 2023): 854–69. http://dx.doi.org/10.1016/j.renene.2023.01.003.
Повний текст джерелаDing, Li, and Lee Spector. "Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits." Entropy 25, no. 1 (January 3, 2023): 93. http://dx.doi.org/10.3390/e25010093.
Повний текст джерелаHe, Yuanzhi, Biao Sheng, Hao Yin, Di Yan, and Yingchao Zhang. "Multi-objective deep reinforcement learning based time-frequency resource allocation for multi-beam satellite communications." China Communications 19, no. 1 (January 2022): 77–91. http://dx.doi.org/10.23919/jcc.2022.01.007.
Повний текст джерелаPark, Bumjin, Cheongwoong Kang, and Jaesik Choi. "Cooperative Multi-Robot Task Allocation with Reinforcement Learning." Applied Sciences 12, no. 1 (December 28, 2021): 272. http://dx.doi.org/10.3390/app12010272.
Повний текст джерелаMutti, Mirco, Mattia Mancassola, and Marcello Restelli. "Unsupervised Reinforcement Learning in Multiple Environments." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7850–58. http://dx.doi.org/10.1609/aaai.v36i7.20754.
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