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Статті в журналах з теми "RL ALGORITHMS"
Lahande, Prathamesh, Parag Kaveri, and Jatinderkumar Saini. "Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment." Informatics 10, no. 3 (August 2, 2023): 64. http://dx.doi.org/10.3390/informatics10030064.
Повний текст джерелаTrella, Anna L., Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, and Susan A. Murphy. "Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines." Algorithms 15, no. 8 (July 22, 2022): 255. http://dx.doi.org/10.3390/a15080255.
Повний текст джерелаRodríguez Sánchez, Francisco, Ildeberto Santos-Ruiz, Joaquín Domínguez-Zenteno, and Francisco Ronay López-Estrada. "Control Applications Using Reinforcement Learning: An Overview." Memorias del Congreso Nacional de Control Automático 5, no. 1 (October 17, 2022): 67–72. http://dx.doi.org/10.58571/cnca.amca.2022.019.
Повний текст джерелаAbbass, Mahmoud Abdelkader Bashery, and Hyun-Soo Kang. "Drone Elevation Control Based on Python-Unity Integrated Framework for Reinforcement Learning Applications." Drones 7, no. 4 (March 24, 2023): 225. http://dx.doi.org/10.3390/drones7040225.
Повний текст джерелаMann, Timothy, and Yoonsuck Choe. "Scaling Up Reinforcement Learning through Targeted Exploration." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 435–40. http://dx.doi.org/10.1609/aaai.v25i1.7929.
Повний текст джерелаCheng, Richard, Gábor Orosz, Richard M. Murray, and 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 (July 17, 2019): 3387–95. http://dx.doi.org/10.1609/aaai.v33i01.33013387.
Повний текст джерелаKirsch, Louis, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, and Yutian Chen. "Introducing Symmetries to Black Box Meta Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7202–10. http://dx.doi.org/10.1609/aaai.v36i7.20681.
Повний текст джерелаKim, Hyun-Su, and Uksun Kim. "Development of a Control Algorithm for a Semi-Active Mid-Story Isolation System Using Reinforcement Learning." Applied Sciences 13, no. 4 (February 4, 2023): 2053. http://dx.doi.org/10.3390/app13042053.
Повний текст джерелаPrakash, Kritika, Fiza Husain, Praveen Paruchuri, and Sujit Gujar. "How Private Is Your RL Policy? An Inverse RL Based Analysis Framework." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 8009–16. http://dx.doi.org/10.1609/aaai.v36i7.20772.
Повний текст джерелаNiazi, Abdolkarim, Norizah Redzuan, Raja Ishak Raja Hamzah, and Sara Esfandiari. "Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making." Advanced Materials Research 566 (September 2012): 572–79. http://dx.doi.org/10.4028/www.scientific.net/amr.566.572.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаMarknadens 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.
Повний текст джерелаЧастини книг з теми "RL ALGORITHMS"
Ahlawat, Samit. "Recent RL Algorithms." In Reinforcement Learning for Finance, 349–402. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8835-1_6.
Повний текст джерелаNandy, Abhishek, and Manisha Biswas. "RL Theory and Algorithms." In Reinforcement Learning, 19–69. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3285-9_2.
Повний текст джерелаHahn, Ernst Moritz, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, and Dominik Wojtczak. "Mungojerrie: Linear-Time Objectives in Model-Free Reinforcement Learning." In 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.
Повний текст джерелаRamponi, Giorgia. "Learning in the Presence of Multiple Agents." In Special Topics in Information Technology, 93–103. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_8.
Повний текст джерелаMetelli, Alberto Maria. "Configurable Environments in Reinforcement Learning: An Overview." In Special Topics in Information Technology, 101–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_9.
Повний текст джерелаGros, Timo P., Holger Hermanns, Jörg Hoffmann, Michaela Klauck, Maximilian A. Köhl, and Verena Wolf. "MoGym: Using Formal Models for Training and Verifying Decision-making Agents." In Computer Aided Verification, 430–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_21.
Повний текст джерелаDu, Huaiyu, and Rafał Jóźwiak. "Representation of Observations in Reinforcement Learning for Playing Arcade Fighting Game." In Digital Interaction and Machine Intelligence, 45–55. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37649-8_5.
Повний текст джерелаBugaenko, 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." In Software Engineering Application in Informatics, 15–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90318-3_2.
Повний текст джерелаWang, Dasong, and Roland Snooks. "Artificial Intuitions of Generative Design: An Approach Based on Reinforcement Learning." In Proceedings of the 2020 DigitalFUTURES, 189–98. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_18.
Повний текст джерелаZhang, Sizhe, Haitao Wang, Jian Wen, and Hejun Wu. "A Deep RL Algorithm for Location Optimization of Regional Express Distribution Center Using IoT Data." In Lecture Notes in Electrical Engineering, 377–84. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0416-7_38.
Повний текст джерелаТези доповідей конференцій з теми "RL ALGORITHMS"
Simão, Thiago D. "Safe and Sample-Efficient Reinforcement Learning Algorithms for Factored Environments." In 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.
Повний текст джерелаChrabąszcz, Patryk, Ilya Loshchilov, and Frank Hutter. "Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari." In 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.
Повний текст джерелаArusoaie, Andrei, David Nowak, Vlad Rusu, and Dorel Lucanu. "A Certified Procedure for RL Verification." In 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2017. http://dx.doi.org/10.1109/synasc.2017.00031.
Повний текст джерелаGajane, Pratik, Peter Auer, and Ronald Ortner. "Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms." In 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.
Повний текст джерелаLin, Zichuan, Tianqi Zhao, Guangwen Yang, and Lintao Zhang. "Episodic Memory Deep Q-Networks." In 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.
Повний текст джерелаMartin, Jarryd, Suraj Narayanan S., Tom Everitt, and Marcus Hutter. "Count-Based Exploration in Feature Space for Reinforcement Learning." In 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.
Повний текст джерелаDa Silva, Felipe Leno, and Anna Helena Reali Costa. "Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/ctd.2020.11360.
Повний текст джерелаGao, Yang, Christian M. Meyer, Mohsen Mesgar, and Iryna Gurevych. "Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation." In 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.
Повний текст джерелаZhao, Enmin, Shihong Deng, Yifan Zang, Yongxin Kang, Kai Li, and Junliang Xing. "Potential Driven Reinforcement Learning for Hard Exploration Tasks." In 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.
Повний текст джерелаSarafian, Elad, Aviv Tamar, and Sarit Kraus. "Constrained Policy Improvement for Efficient Reinforcement Learning." In 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.
Повний текст джерелаЗвіти організацій з теми "RL ALGORITHMS"
A Decision-Making Method for Connected Autonomous Driving Based on Reinforcement Learning. SAE International, December 2020. http://dx.doi.org/10.4271/2020-01-5154.
Повний текст джерела