Journal articles on the topic '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.
Full textKim, 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.
Full textDrugan, 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.
Full textPerez, 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.
Full textNguyen, 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.
Full textGarcía, 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.
Full textYamamoto, 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.
Full textGarcía, 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.
Full textSchneider, 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.
Full textFerreira, 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.
Full textParisi, 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.
Full text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.
Full textKOBAYASHI, 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.
Full textWestbrink, 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.
Full textRuiz-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.
Full textZou, 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.
Full textQin, 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.
Full textComsa, 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.
Full textLi, 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.
Full textShresthamali, 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.
Full textChen, 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.
Full textBi, 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.
Full textLepenioti, 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.
Full textLi, 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.
Full textMannion, 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.
Full textHuo, 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.
Full textZhang, 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.
Full textWang, 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.
Full textYAMADA, 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.
Full textAbdelfattah, 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.
Full textGarcía, 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.
Full textWang, 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.
Full textBeeks, 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.
Full textQin, 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.
Full textStudley, 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.
Full textMa, 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.
Full textYliniemi, 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.
Full textLim, 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.
Full textSong, 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.
Full textJyothi, 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.
Full textHu, 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.
Full textTao, 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.
Full textLuo, 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.
Full textM. 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.
Full textKozjek, 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.
Full textWang, 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.
Full textDing, 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.
Full textHe, 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.
Full textPark, 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.
Full textMutti, 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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