Literatura académica sobre el tema "RL ALGORITHMS"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "RL ALGORITHMS".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "RL ALGORITHMS"
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 completoTesis sobre el tema "RL ALGORITHMS"
Marcus, Elwin. "Simulating market maker behaviour using Deep Reinforcement Learning to understand market microstructure". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240682.
Texto completoMarknadens mikrostruktur studerar hur utbytet av finansiella tillgångar sker enligt explicita regler. Algoritmisk och högfrekvenshandel har förändrat moderna finansmarknaders strukturer under de senaste 5 till 10 åren. Detta har även påverkat pålitligheten hos tidigare använda metoder från exempelvis ekonometri för att studera marknadens mikrostruktur. Maskininlärning och Reinforcement Learning har blivit mer populära, med många olika användningsområden både inom finans och andra fält. Inom finansfältet har dessa typer av metoder använts främst inom handel och optimal exekvering av ordrar. I denna uppsats kombineras både Reinforcement Learning och marknadens mikrostruktur, för att simulera en aktiemarknad baserad på NASDAQ i Norden. Där tränas market maker - agenter via Reinforcement Learning med målet att förstå marknadens mikrostruktur som uppstår via agenternas interaktioner. I denna uppsats utvärderas och testas agenterna på en dealer – marknad tillsammans med en limit - orderbok. Vilket särskiljer denna studie tillsammans med de två algoritmerna DQN och PPO från tidigare studier. Främst har stokastisk optimering använts för liknande problem i tidigare studier. Agenterna lyckas framgångsrikt med att återskapa egenskaper hos finansiella tidsserier som återgång till medelvärdet och avsaknad av linjär autokorrelation. Agenterna lyckas också med att vinna över slumpmässiga strategier, med maximal vinst på 200%. Slutgiltigen lyckas även agenterna med att visa annan handelsdynamik som förväntas ske på en verklig marknad. Huvudsakligen: kluster av spreads, optimal hantering av aktielager och en minskning av spreads under simuleringarna. Detta visar att Reinforcement Learning med PPO eller DQN är relevanta val vid modellering av marknadens mikrostruktur.
ALI, FAIZ MOHAMMAD. "CART POLE SYSTEM ANALYSIS AND CONTROL USING MACHINE LEARNING ALGORITHMS". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19298.
Texto completoCapítulos de libros sobre el tema "RL ALGORITHMS"
Ahlawat, Samit. "Recent RL Algorithms". En Reinforcement Learning for Finance, 349–402. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8835-1_6.
Texto completoNandy, Abhishek y Manisha Biswas. "RL Theory and Algorithms". En Reinforcement Learning, 19–69. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3285-9_2.
Texto completoHahn, Ernst Moritz, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi y Dominik Wojtczak. "Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning". En Tools and Algorithms for the Construction and Analysis of Systems, 527–45. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30823-9_27.
Texto completoRamponi, Giorgia. "Learning in the Presence of Multiple Agents". En Special Topics in Information Technology, 93–103. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_8.
Texto completoMetelli, Alberto Maria. "Configurable Environments in Reinforcement Learning: An Overview". En Special Topics in Information Technology, 101–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_9.
Texto completoGros, Timo P., Holger Hermanns, Jörg Hoffmann, Michaela Klauck, Maximilian A. Köhl y Verena Wolf. "MoGym: Using Formal Models for Training and Verifying Decision-making Agents". En Computer Aided Verification, 430–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_21.
Texto completoDu, Huaiyu y Rafał Jóźwiak. "Representation of Observations in Reinforcement Learning for Playing Arcade Fighting Game". En Digital Interaction and Machine Intelligence, 45–55. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37649-8_5.
Texto completoBugaenko, Andrey A. "Replacing the Reinforcement Learning (RL) to the Auto Reinforcement Learning (AutoRL) Algorithms to Find the Optimal Structure of Business Processes in the Bank". En Software Engineering Application in Informatics, 15–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90318-3_2.
Texto completoWang, Dasong y Roland Snooks. "Artificial Intuitions of Generative Design: An Approach Based on Reinforcement Learning". En Proceedings of the 2020 DigitalFUTURES, 189–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_18.
Texto completoZhang, Sizhe, Haitao Wang, Jian Wen y Hejun Wu. "A Deep RL Algorithm for Location Optimization of Regional Express Distribution Center Using IoT Data". En Lecture Notes in Electrical Engineering, 377–84. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_38.
Texto completoActas de conferencias sobre el tema "RL ALGORITHMS"
Simão, Thiago D. "Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/919.
Texto completoChrabąszcz, Patryk, Ilya Loshchilov y Frank Hutter. "Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/197.
Texto completoArusoaie, Andrei, David Nowak, Vlad Rusu y Dorel Lucanu. "A Certified Procedure for RL Verification". En 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2017. http://dx.doi.org/10.1109/synasc.2017.00031.
Texto completoGajane, Pratik, Peter Auer y Ronald Ortner. "Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/413.
Texto completoLin, Zichuan, Tianqi Zhao, Guangwen Yang y Lintao Zhang. "Episodic Memory Deep Q-Networks". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/337.
Texto completoMartin, Jarryd, Suraj Narayanan S., Tom Everitt y Marcus Hutter. "Count-Based Exploration in Feature Space for Reinforcement Learning". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/344.
Texto completoDa Silva, Felipe Leno y Anna Helena Reali Costa. "Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning". En Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2020.11360.
Texto completoGao, Yang, Christian M. Meyer, Mohsen Mesgar y Iryna Gurevych. "Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation". En Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/326.
Texto completoZhao, Enmin, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li y Junliang Xing. "Potential Driven Reinforcement Learning for Hard Exploration Tasks". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/290.
Texto completoSarafian, Elad, Aviv Tamar y Sarit Kraus. "Constrained Policy Improvement for Efficient Reinforcement Learning". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/396.
Texto completoInformes sobre el tema "RL ALGORITHMS"
A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, diciembre de 2020. http://dx.doi.org/10.4271/2020-01-5154.
Texto completo