Artículos de revistas sobre el tema "Reinforcement learning (Machine learning)"
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Ishii, Shin y Wako Yoshida. "Part 4: Reinforcement learning: Machine learning and natural learning". New Generation Computing 24, n.º 3 (septiembre de 2006): 325–50. http://dx.doi.org/10.1007/bf03037338.
Texto completoWang, Zizhuang. "Temporal-Related Convolutional-Restricted-Boltzmann-Machine Capable of Learning Relational Order via Reinforcement Learning Procedure". International Journal of Machine Learning and Computing 7, n.º 1 (febrero de 2017): 1–8. http://dx.doi.org/10.18178/ijmlc.2017.7.1.610.
Texto completoButlin, Patrick. "Machine Learning, Functions and Goals". Croatian journal of philosophy 22, n.º 66 (27 de diciembre de 2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.
Texto completoMartín-Guerrero, José D. y Lucas Lamata. "Reinforcement Learning and Physics". Applied Sciences 11, n.º 18 (16 de septiembre de 2021): 8589. http://dx.doi.org/10.3390/app11188589.
Texto completoLiu, Yicen, Yu Lu, Xi Li, Wenxin Qiao, Zhiwei Li y Donghao Zhao. "SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches". IEEE Communications Letters 25, n.º 6 (junio de 2021): 1926–30. http://dx.doi.org/10.1109/lcomm.2021.3061991.
Texto completoPopkov, Yuri S., Yuri A. Dubnov y Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning". Mathematics 11, n.º 17 (23 de agosto de 2023): 3651. http://dx.doi.org/10.3390/math11173651.
Texto completoCrawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi y Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines". Quantum Information and Computation 18, n.º 1&2 (febrero de 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.
Texto completoLamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics". Photonics 8, n.º 2 (28 de enero de 2021): 33. http://dx.doi.org/10.3390/photonics8020033.
Texto completoSahu, Santosh Kumar, Anil Mokhade y Neeraj Dhanraj Bokde. "An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges". Applied Sciences 13, n.º 3 (2 de febrero de 2023): 1956. http://dx.doi.org/10.3390/app13031956.
Texto completoFang, Qiang, Wenzhuo Zhang y Xitong Wang. "Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine". Electronics 10, n.º 16 (18 de agosto de 2021): 1997. http://dx.doi.org/10.3390/electronics10161997.
Texto completoSarikhani, Rahil y Farshid Keynia. "Cooperative Spectrum Sensing Meets Machine Learning: Deep Reinforcement Learning Approach". IEEE Communications Letters 24, n.º 7 (julio de 2020): 1459–62. http://dx.doi.org/10.1109/lcomm.2020.2984430.
Texto completoAlDahoul, Nouar, Zaw Zaw Htike y Rini Akmeliawati. "Hierarchical extreme learning machine based reinforcement learning for goal localization". IOP Conference Series: Materials Science and Engineering 184 (marzo de 2017): 012055. http://dx.doi.org/10.1088/1757-899x/184/1/012055.
Texto completoMcPartland, Michelle y Marcus Gallagher. "Learning to be a Bot: Reinforcement Learning in Shooter Games". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4, n.º 1 (27 de septiembre de 2021): 78–83. http://dx.doi.org/10.1609/aiide.v4i1.18676.
Texto completoZine, Mohamed, Fouzi Harrou, Mohammed Terbeche, Mohammed Bellahcene, Abdelkader Dairi y Ying Sun. "E-Learning Readiness Assessment Using Machine Learning Methods". Sustainability 15, n.º 11 (1 de junio de 2023): 8924. http://dx.doi.org/10.3390/su15118924.
Texto completoZhang, Ziyu. "Basic things about reinforcement learning". Applied and Computational Engineering 6, n.º 1 (14 de junio de 2023): 280–84. http://dx.doi.org/10.54254/2755-2721/6/20230788.
Texto completoYang, Yanxiang, Jiang Hu, Dana Porter, Thomas Marek, Kevin Heflin y Hongxin Kong. "Deep Reinforcement Learning-Based Irrigation Scheduling". Transactions of the ASABE 63, n.º 3 (2020): 549–56. http://dx.doi.org/10.13031/trans.13633.
Texto completoDiGiovanna, J., B. Mahmoudi, J. Fortes, J. C. Principe y J. C. Sanchez. "Coadaptive Brain–Machine Interface via Reinforcement Learning". IEEE Transactions on Biomedical Engineering 56, n.º 1 (enero de 2009): 54–64. http://dx.doi.org/10.1109/tbme.2008.926699.
Texto completoNAKATANI, Masayuki, Zeyuan SUN y Yutaka UCHIMURA. "Intelligent Construction Machine by Deep Reinforcement Learning". Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—G03. http://dx.doi.org/10.1299/jsmermd.2017.2p2-g03.
Texto completoMahdi, Hiba. "Blockchain and Machine Learning as Deep Reinforcement". Wasit Journal of Computer and Mathematics Science 2, n.º 1 (31 de marzo de 2023): 72–84. http://dx.doi.org/10.31185/wjcm.103.
Texto completoHoshino, Yukinobu y Katsuari Kamei. "Effective Use of Learning Knowledge by FEERL". Journal of Advanced Computational Intelligence and Intelligent Informatics 7, n.º 1 (20 de febrero de 2003): 6–9. http://dx.doi.org/10.20965/jaciii.2003.p0006.
Texto completoJadhav, Rutuja. "Tracking Locomotion using Reinforcement Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 7 (31 de julio de 2022): 1777–83. http://dx.doi.org/10.22214/ijraset.2022.45509.
Texto completoEckardt, Jan-Niklas, Karsten Wendt, Martin Bornhäuser y Jan Moritz Middeke. "Reinforcement Learning for Precision Oncology". Cancers 13, n.º 18 (15 de septiembre de 2021): 4624. http://dx.doi.org/10.3390/cancers13184624.
Texto completoKaelbling, L. P., M. L. Littman y A. W. Moore. "Reinforcement Learning: A Survey". Journal of Artificial Intelligence Research 4 (1 de mayo de 1996): 237–85. http://dx.doi.org/10.1613/jair.301.
Texto completoTizhoosh, Hamid R. "Opposition-Based Reinforcement Learning". Journal of Advanced Computational Intelligence and Intelligent Informatics 10, n.º 4 (20 de julio de 2006): 578–85. http://dx.doi.org/10.20965/jaciii.2006.p0578.
Texto completoMeng, Terry Lingze y Matloob Khushi. "Reinforcement Learning in Financial Markets". Data 4, n.º 3 (28 de julio de 2019): 110. http://dx.doi.org/10.3390/data4030110.
Texto completoSenthil, Chandran y Ranjitharamasamy Sudhakara Pandian. "Proactive Maintenance Model Using Reinforcement Learning Algorithm in Rubber Industry". Processes 10, n.º 2 (14 de febrero de 2022): 371. http://dx.doi.org/10.3390/pr10020371.
Texto completoSaeed, Shaheer U., Yunguan Fu, Vasilis Stavrinides, Zachary M. C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan et al. "Image quality assessment for machine learning tasks using meta-reinforcement learning". Medical Image Analysis 78 (mayo de 2022): 102427. http://dx.doi.org/10.1016/j.media.2022.102427.
Texto completoAlDahoul, Nouar y ZawZaw Htike. "Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting". International Journal of ADVANCED AND APPLIED SCIENCES 6, n.º 1 (enero de 2019): 106–13. http://dx.doi.org/10.21833/ijaas.2019.01.015.
Texto completoCalabuig, J. M., H. Falciani y E. A. Sánchez-Pérez. "Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets". Neurocomputing 398 (julio de 2020): 172–84. http://dx.doi.org/10.1016/j.neucom.2020.02.052.
Texto completoZhang, Junhao y Yifei Lei. "Deep Reinforcement Learning for Stock Prediction". Scientific Programming 2022 (30 de abril de 2022): 1–9. http://dx.doi.org/10.1155/2022/5812546.
Texto completoXu, Zhe, Ivan Gavran, Yousef Ahmad, Rupak Majumdar, Daniel Neider, Ufuk Topcu y Bo Wu. "Joint Inference of Reward Machines and Policies for Reinforcement Learning". Proceedings of the International Conference on Automated Planning and Scheduling 30 (1 de junio de 2020): 590–98. http://dx.doi.org/10.1609/icaps.v30i1.6756.
Texto completoKoker, Thomas E. y Dimitrios Koutmos. "Cryptocurrency Trading Using Machine Learning". Journal of Risk and Financial Management 13, n.º 8 (10 de agosto de 2020): 178. http://dx.doi.org/10.3390/jrfm13080178.
Texto completoChen, Irene Y., Shalmali Joshi, Marzyeh Ghassemi y Rajesh Ranganath. "Probabilistic Machine Learning for Healthcare". Annual Review of Biomedical Data Science 4, n.º 1 (20 de julio de 2021): 393–415. http://dx.doi.org/10.1146/annurev-biodatasci-092820-033938.
Texto completoEvseenko, Alla y Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum". Transaction of Scientific Papers of the Novosibirsk State Technical University, n.º 1-2 (26 de agosto de 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.
Texto completoShailaja, Dr M., Nune Vinaya Reddy, Ambati Srujani y Cherukuthota Upeksha Reddy. "Playing Tetris with Reinforcement Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 2088–95. http://dx.doi.org/10.22214/ijraset.2022.44208.
Texto completoDandoti, Sarosh. "Learning to Survive using Reinforcement Learning with MLAgents". International Journal for Research in Applied Science and Engineering Technology 10, n.º 7 (31 de julio de 2022): 3009–14. http://dx.doi.org/10.22214/ijraset.2022.45526.
Texto completoLockwood, Owen y Mei Si. "Reinforcement Learning with Quantum Variational Circuit". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, n.º 1 (1 de octubre de 2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.
Texto completoMeera, A. y S. Swamynathan. "Queue Based Q-Learning for Efficient Resource Provisioning in Cloud Data Centers". International Journal of Intelligent Information Technologies 11, n.º 4 (octubre de 2015): 37–54. http://dx.doi.org/10.4018/ijiit.2015100103.
Texto completoOrgován, László, Tamás Bécsi y Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning". Periodica Polytechnica Transportation Engineering 49, n.º 3 (1 de septiembre de 2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.
Texto completoMondal, Shanka Subhra, Nikhil Sheoran y Subrata Mitra. "Scheduling of Time-Varying Workloads Using Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 9000–9008. http://dx.doi.org/10.1609/aaai.v35i10.17088.
Texto completoLürig, Christoph. "Learning Machine Learning with a Game". European Conference on Games Based Learning 16, n.º 1 (29 de septiembre de 2022): 316–23. http://dx.doi.org/10.34190/ecgbl.16.1.481.
Texto completoShaveta. "A review on machine learning". International Journal of Science and Research Archive 9, n.º 1 (30 de mayo de 2023): 281–85. http://dx.doi.org/10.30574/ijsra.2023.9.1.0410.
Texto completoWang, Canjun, Zhao Li, Tong Chen, Ruishuang Wang y Zhengyu Ju. "Research on the Application of Prompt Learning Pretrained Language Model in Machine Translation Task with Reinforcement Learning". Electronics 12, n.º 16 (9 de agosto de 2023): 3391. http://dx.doi.org/10.3390/electronics12163391.
Texto completoBelozerov, Ilya Andreevich y Vladimir Anatolievich Sudakov. "Reinforcement Machine Learning for Solving Mathematical Programming Problems". Keldysh Institute Preprints, n.º 36 (2022): 1–14. http://dx.doi.org/10.20948/prepr-2022-36.
Texto completoCappart, Quentin, Emmanuel Goutierre, David Bergman y Louis-Martin Rousseau. "Improving Optimization Bounds Using Machine Learning: Decision Diagrams Meet Deep Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 1443–51. http://dx.doi.org/10.1609/aaai.v33i01.33011443.
Texto completoR. Merina, Queentin. "Use of reinforcement learning algorithms to optimize control strategies for single machine systems". i-manager’s Journal on Instrumentation and Control Engineering 10, n.º 2 (2022): 36. http://dx.doi.org/10.26634/jic.10.2.19357.
Texto completoElizarov, Artem Aleksandrovich y Evgenii Viktorovich Razinkov. "Image Classification Using Reinforcement Learning". Russian Digital Libraries Journal 23, n.º 6 (12 de mayo de 2020): 1172–91. http://dx.doi.org/10.26907/1562-5419-2020-23-6-1172-1191.
Texto completoJiang, Weihang. "Applications of machine learning in neuroscience and inspiration of reinforcement learning for computational neuroscience". Applied and Computational Engineering 4, n.º 1 (14 de junio de 2023): 473–78. http://dx.doi.org/10.54254/2755-2721/4/2023308.
Texto completoKong, Xiang, Zhaopeng Tu, Shuming Shi, Eduard Hovy y Tong Zhang. "Neural Machine Translation with Adequacy-Oriented Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 6618–25. http://dx.doi.org/10.1609/aaai.v33i01.33016618.
Texto completoDurgut, Rafet, Mehmet Emin Aydin y Abdur Rakib. "Transfer Learning for Operator Selection: A Reinforcement Learning Approach". Algorithms 15, n.º 1 (17 de enero de 2022): 24. http://dx.doi.org/10.3390/a15010024.
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