Artículos de revistas sobre el tema "RL ALGORITHMS"
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Lahande, Prathamesh, Parag Kaveri y Jatinderkumar Saini. "Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment". Informatics 10, n.º 3 (2 de agosto de 2023): 64. http://dx.doi.org/10.3390/informatics10030064.
Texto completoTrella, Anna L., Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez y Susan A. Murphy. "Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines". Algorithms 15, n.º 8 (22 de julio de 2022): 255. http://dx.doi.org/10.3390/a15080255.
Texto completoRodríguez Sánchez, Francisco, Ildeberto Santos-Ruiz, Joaquín Domínguez-Zenteno y Francisco Ronay López-Estrada. "Control Applications Using Reinforcement Learning: An Overview". Memorias del Congreso Nacional de Control Automático 5, n.º 1 (17 de octubre de 2022): 67–72. http://dx.doi.org/10.58571/cnca.amca.2022.019.
Texto completoAbbass, Mahmoud Abdelkader Bashery y Hyun-Soo Kang. "Drone Elevation Control Based on Python-Unity Integrated Framework for Reinforcement Learning Applications". Drones 7, n.º 4 (24 de marzo de 2023): 225. http://dx.doi.org/10.3390/drones7040225.
Texto completoMann, Timothy y Yoonsuck Choe. "Scaling Up Reinforcement Learning through Targeted Exploration". Proceedings of the AAAI Conference on Artificial Intelligence 25, n.º 1 (4 de agosto de 2011): 435–40. http://dx.doi.org/10.1609/aaai.v25i1.7929.
Texto completoCheng, Richard, Gábor Orosz, Richard M. Murray y Joel W. Burdick. "End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3387–95. http://dx.doi.org/10.1609/aaai.v33i01.33013387.
Texto completoKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh y Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Texto completoKim, Hyun-Su y Uksun Kim. "Development of a Control Algorithm for a Semi-Active Mid-Story Isolation System Using Reinforcement Learning". Applied Sciences 13, n.º 4 (4 de febrero de 2023): 2053. http://dx.doi.org/10.3390/app13042053.
Texto completoPrakash, Kritika, Fiza Husain, Praveen Paruchuri y Sujit Gujar. "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 8009–16. http://dx.doi.org/10.1609/aaai.v36i7.20772.
Texto completoNiazi, Abdolkarim, Norizah Redzuan, Raja Ishak Raja Hamzah y Sara Esfandiari. "Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making". Advanced Materials Research 566 (septiembre de 2012): 572–79. http://dx.doi.org/10.4028/www.scientific.net/amr.566.572.
Texto completoMu, Tong, Georgios Theocharous, David Arbour y Emma Brunskill. "Constraint Sampling Reinforcement Learning: Incorporating Expertise for Faster Learning". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 7 (28 de junio de 2022): 7841–49. http://dx.doi.org/10.1609/aaai.v36i7.20753.
Texto completoKołota, Jakub y Turhan Can Kargin. "Comparison of Various Reinforcement Learning Environments in the Context of Continuum Robot Control". Applied Sciences 13, n.º 16 (11 de agosto de 2023): 9153. http://dx.doi.org/10.3390/app13169153.
Texto completoJang, Sun-Ho, Woo-Jin Ahn, Yu-Jin Kim, Hyung-Gil Hong, Dong-Sung Pae y Myo-Taeg Lim. "Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles". Electronics 12, n.º 18 (6 de septiembre de 2023): 3773. http://dx.doi.org/10.3390/electronics12183773.
Texto completoPeng, Zhiyong, Changlin Han, Yadong Liu y Zongtan Zhou. "Weighted Policy Constraints for Offline Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 8 (26 de junio de 2023): 9435–43. http://dx.doi.org/10.1609/aaai.v37i8.26130.
Texto completoTessler, Chen, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik y Shie Mannor. "Reinforcement Learning for Datacenter Congestion Control". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 11 (28 de junio de 2022): 12615–21. http://dx.doi.org/10.1609/aaai.v36i11.21535.
Texto completoJIANG, JU, MOHAMED S. KAMEL y LEI CHEN. "AGGREGATION OF MULTIPLE REINFORCEMENT LEARNING ALGORITHMS". International Journal on Artificial Intelligence Tools 15, n.º 05 (octubre de 2006): 855–61. http://dx.doi.org/10.1142/s0218213006002990.
Texto completoChen, Feng, Chenghe Wang, Fuxiang Zhang, Hao Ding, Qiaoyong Zhong, Shiliang Pu y Zongzhang Zhang. "Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16182–83. http://dx.doi.org/10.1609/aaai.v37i13.26951.
Texto completoGuo, Kun y Qishan Zhang. "A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem". International Journal of Information Technology & Decision Making 16, n.º 05 (septiembre de 2017): 1339–57. http://dx.doi.org/10.1142/s0219622014500126.
Texto completoPadakandla, Sindhu. "A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments". ACM Computing Surveys 54, n.º 6 (julio de 2021): 1–25. http://dx.doi.org/10.1145/3459991.
Texto completoGaon, Maor y Ronen Brafman. "Reinforcement Learning with Non-Markovian Rewards". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3980–87. http://dx.doi.org/10.1609/aaai.v34i04.5814.
Texto completoSun, Peiquan, Wengang Zhou y Houqiang Li. "Attentive Experience Replay". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5900–5907. http://dx.doi.org/10.1609/aaai.v34i04.6049.
Texto completoChen, Zaiwei. "A Unified Lyapunov Framework for Finite-Sample Analysis of Reinforcement Learning Algorithms". ACM SIGMETRICS Performance Evaluation Review 50, n.º 3 (30 de diciembre de 2022): 12–15. http://dx.doi.org/10.1145/3579342.3579346.
Texto completoYau, Kok-Lim Alvin, Geong-Sen Poh, Su Fong Chien y Hasan A. A. Al-Rawi. "Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms". Scientific World Journal 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/209810.
Texto completoTessler, Chen, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik y Shie Mannor. "Reinforcement Learning for Datacenter Congestion Control". ACM SIGMETRICS Performance Evaluation Review 49, n.º 2 (17 de enero de 2022): 43–46. http://dx.doi.org/10.1145/3512798.3512815.
Texto completoJin, Zengwang, Menglu Ma, Shuting Zhang, Yanyan Hu, Yanning Zhang y Changyin Sun. "Secure State Estimation of Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA". Electronics 11, n.º 19 (1 de octubre de 2022): 3161. http://dx.doi.org/10.3390/electronics11193161.
Texto completoLi, Shaodong, Xiaogang Yuan y Jie Niu. "Robotic Peg-in-Hole Assembly Strategy Research Based on Reinforcement Learning Algorithm". Applied Sciences 12, n.º 21 (3 de noviembre de 2022): 11149. http://dx.doi.org/10.3390/app122111149.
Texto completoPan, Yaozong, Jian Zhang, Chunhui Yuan y Haitao Yang. "Supervised Reinforcement Learning via Value Function". Symmetry 11, n.º 4 (24 de abril de 2019): 590. http://dx.doi.org/10.3390/sym11040590.
Texto completoKabanda, Professor Gabriel, Colletor Tendeukai Chipfumbu y Tinashe Chingoriwo. "A Reinforcement Learning Paradigm for Cybersecurity Education and Training". Oriental journal of computer science and technology 16, n.º 01 (30 de mayo de 2023): 12–45. http://dx.doi.org/10.13005/ojcst16.01.02.
Texto completoYousif, Ayman Basheer, Hassan Jaleel Hassan y Gaida Muttasher. "Applying reinforcement learning for random early detaction algorithm in adaptive queue management systems". Indonesian Journal of Electrical Engineering and Computer Science 26, n.º 3 (1 de junio de 2022): 1684. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1684-1691.
Texto completoSzita, István y András Lörincz. "Learning Tetris Using the Noisy Cross-Entropy Method". Neural Computation 18, n.º 12 (diciembre de 2006): 2936–41. http://dx.doi.org/10.1162/neco.2006.18.12.2936.
Texto completoYe, Weicheng y Dangxing Chen. "Analysis of Performance Measure in Q Learning with UCB Exploration". Mathematics 10, n.º 4 (12 de febrero de 2022): 575. http://dx.doi.org/10.3390/math10040575.
Texto completoLin, Xingbin, Deyu Yuan y Xifei Li. "Reinforcement Learning with Dual Safety Policies for Energy Savings in Building Energy Systems". Buildings 13, n.º 3 (21 de febrero de 2023): 580. http://dx.doi.org/10.3390/buildings13030580.
Texto completoLi, Luchen y A. Aldo Faisal. "Bayesian Distributional Policy Gradients". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 10 (18 de mayo de 2021): 8429–37. http://dx.doi.org/10.1609/aaai.v35i10.17024.
Texto completoGrewal, Yashvir S., Frits De Nijs y Sarah Goodwin. "Evaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 18 (18 de mayo de 2021): 15785–86. http://dx.doi.org/10.1609/aaai.v35i18.17889.
Texto completoVillalpando-Hernandez, Rafaela, Cesar Vargas-Rosales y David Munoz-Rodriguez. "Localization Algorithm for 3D Sensor Networks: A Recursive Data Fusion Approach". Sensors 21, n.º 22 (17 de noviembre de 2021): 7626. http://dx.doi.org/10.3390/s21227626.
Texto completoZhao, Richard y Duane Szafron. "Learning Character Behaviors Using Agent Modeling in Games". Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 5, n.º 1 (16 de octubre de 2009): 179–85. http://dx.doi.org/10.1609/aiide.v5i1.12369.
Texto completoHu y Xu. "Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation". Information 10, n.º 11 (2 de noviembre de 2019): 341. http://dx.doi.org/10.3390/info10110341.
Texto completoShen, Haocheng, Jason Yosinski, Petar Kormushev, Darwin G. Caldwell y Hod Lipson. "Learning Fast Quadruped Robot Gaits with the RL PoWER Spline Parameterization". Cybernetics and Information Technologies 12, n.º 3 (1 de septiembre de 2012): 66–75. http://dx.doi.org/10.2478/cait-2012-0022.
Texto completoShaposhnikova, Sofiia y Dmytro Omelian. "TOWARDS EFFECTIVE STRATEGIES FOR MOBILE ROBOT USING REINFORCEMENT LEARNING AND GRAPH ALGORITHMS". Automation of technological and business processes 15, n.º 2 (19 de junio de 2023): 24–34. http://dx.doi.org/10.15673/atbp.v15i2.2522.
Texto completoLiao, Hanlin. "Urban Intersection Simulation and Verification via Deep Reinforcement Learning Algorithms". Journal of Physics: Conference Series 2435, n.º 1 (1 de febrero de 2023): 012019. http://dx.doi.org/10.1088/1742-6596/2435/1/012019.
Texto completoDing, Yuhao, Ming Jin y Javad Lavaei. "Non-stationary Risk-Sensitive Reinforcement Learning: Near-Optimal Dynamic Regret, Adaptive Detection, and Separation Design". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 6 (26 de junio de 2023): 7405–13. http://dx.doi.org/10.1609/aaai.v37i6.25901.
Texto completoSarkar, Soumyadip. "Quantitative Trading using Deep Q Learning". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 731–38. http://dx.doi.org/10.22214/ijraset.2023.50170.
Texto completoZhang, Ningyan. "Analysis of reinforce learning in medical treatment". Applied and Computational Engineering 5, n.º 1 (14 de junio de 2023): 48–53. http://dx.doi.org/10.54254/2755-2721/5/20230527.
Texto completoPuspitasari, Annisa Anggun y Byung Moo Lee. "A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications". Sensors 23, n.º 5 (24 de febrero de 2023): 2554. http://dx.doi.org/10.3390/s23052554.
Texto completoDelipetrev, Blagoj, Andreja Jonoski y Dimitri P. Solomatine. "A novel nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL) algorithm for multipurpose reservoir optimization". Journal of Hydroinformatics 19, n.º 1 (17 de septiembre de 2016): 47–61. http://dx.doi.org/10.2166/hydro.2016.243.
Texto completoWang, Mengmei. "Optimizing Multitask Assignment of Internet of Things Devices by Reinforcement Learning in Mobile Crowdsensing Scenes". Security and Communication Networks 2022 (17 de agosto de 2022): 1–10. http://dx.doi.org/10.1155/2022/6202237.
Texto completoГайнетдинов, А. Ф. "NeRF IN REINFORCEMENT LEARNING FOR IMAGE RECOGNITION". Южно-Сибирский научный вестник, n.º 2(48) (30 de abril de 2023): 63–72. http://dx.doi.org/10.25699/sssb.2023.48.2.011.
Texto completoNicola, Marcel y Claudiu-Ionel Nicola. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning". Mathematics 10, n.º 24 (9 de diciembre de 2022): 4667. http://dx.doi.org/10.3390/math10244667.
Texto completoYou, Haoyi, Beichen Yu, Haiming Jin, Zhaoxing Yang y Jiahui Sun. "User-Oriented Robust Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 12 (26 de junio de 2023): 15269–77. http://dx.doi.org/10.1609/aaai.v37i12.26781.
Texto completoYang, Bin, Muhammad Haseeb Arshad y Qing Zhao. "Packet-Level and Flow-Level Network Intrusion Detection Based on Reinforcement Learning and Adversarial Training". Algorithms 15, n.º 12 (30 de noviembre de 2022): 453. http://dx.doi.org/10.3390/a15120453.
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