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Artykuły w czasopismach na temat "Reinforcement learning (Machine learning)"
Ishii, Shin, i Wako Yoshida. "Part 4: Reinforcement learning: Machine learning and natural learning". New Generation Computing 24, nr 3 (wrzesień 2006): 325–50. http://dx.doi.org/10.1007/bf03037338.
Pełny tekst źródłaWang, Zizhuang. "Temporal-Related Convolutional-Restricted-Boltzmann-Machine Capable of Learning Relational Order via Reinforcement Learning Procedure". International Journal of Machine Learning and Computing 7, nr 1 (luty 2017): 1–8. http://dx.doi.org/10.18178/ijmlc.2017.7.1.610.
Pełny tekst źródłaButlin, Patrick. "Machine Learning, Functions and Goals". Croatian journal of philosophy 22, nr 66 (27.12.2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.
Pełny tekst źródłaMartín-Guerrero, José D., i Lucas Lamata. "Reinforcement Learning and Physics". Applied Sciences 11, nr 18 (16.09.2021): 8589. http://dx.doi.org/10.3390/app11188589.
Pełny tekst źródłaLiu, Yicen, Yu Lu, Xi Li, Wenxin Qiao, Zhiwei Li i Donghao Zhao. "SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches". IEEE Communications Letters 25, nr 6 (czerwiec 2021): 1926–30. http://dx.doi.org/10.1109/lcomm.2021.3061991.
Pełny tekst źródłaPopkov, Yuri S., Yuri A. Dubnov i Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning". Mathematics 11, nr 17 (23.08.2023): 3651. http://dx.doi.org/10.3390/math11173651.
Pełny tekst źródłaCrawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi i Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines". Quantum Information and Computation 18, nr 1&2 (luty 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.
Pełny tekst źródłaLamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics". Photonics 8, nr 2 (28.01.2021): 33. http://dx.doi.org/10.3390/photonics8020033.
Pełny tekst źródłaSahu, Santosh Kumar, Anil Mokhade i 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, nr 3 (2.02.2023): 1956. http://dx.doi.org/10.3390/app13031956.
Pełny tekst źródłaFang, Qiang, Wenzhuo Zhang i Xitong Wang. "Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine". Electronics 10, nr 16 (18.08.2021): 1997. http://dx.doi.org/10.3390/electronics10161997.
Pełny tekst źródłaRozprawy doktorskie na temat "Reinforcement learning (Machine learning)"
Hengst, Bernhard Computer Science & Engineering Faculty of Engineering UNSW. "Discovering hierarchy in reinforcement learning". Awarded by:University of New South Wales. Computer Science and Engineering, 2003. http://handle.unsw.edu.au/1959.4/20497.
Pełny tekst źródłaTabell, Johnsson Marco, i Ala Jafar. "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents". Thesis, Blekinge Tekniska Högskola, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20218.
Pełny tekst źródłaAkrour, Riad. "Robust Preference Learning-based Reinforcement Learning". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.
Pełny tekst źródłaThe thesis contributions resolves around sequential decision taking and more precisely Reinforcement Learning (RL). Taking its root in Machine Learning in the same way as supervised and unsupervised learning, RL quickly grow in popularity within the last two decades due to a handful of achievements on both the theoretical and applicative front. RL supposes that the learning agent and its environment follow a stochastic Markovian decision process over a state and action space. The process is said of decision as the agent is asked to choose at each time step an action to take. It is said stochastic as the effect of selecting a given action in a given state does not systematically yield the same state but rather defines a distribution over the state space. It is said to be Markovian as this distribution only depends on the current state-action pair. Consequently to the choice of an action, the agent receives a reward. The RL goal is then to solve the underlying optimization problem of finding the behaviour that maximizes the sum of rewards all along the interaction of the agent with its environment. From an applicative point of view, a large spectrum of problems can be cast onto an RL one, from Backgammon (TD-Gammon, was one of Machine Learning first success giving rise to a world class player of advanced level) to decision problems in the industrial and medical world. However, the optimization problem solved by RL depends on the prevous definition of a reward function that requires a certain level of domain expertise and also knowledge of the internal quirks of RL algorithms. As such, the first contribution of the thesis was to propose a learning framework that lightens the requirements made to the user. The latter does not need anymore to know the exact solution of the problem but to only be able to choose between two behaviours exhibited by the agent, the one that matches more closely the solution. Learning is interactive between the agent and the user and resolves around the three main following points: i) The agent demonstrates a behaviour ii) The user compares it w.r.t. to the current best one iii) The agent uses this feedback to update its preference model of the user and uses it to find the next behaviour to demonstrate. To reduce the number of required interactions before finding the optimal behaviour, the second contribution of the thesis was to define a theoretically sound criterion making the trade-off between the sometimes contradicting desires of complying with the user's preferences and demonstrating sufficiently different behaviours. The last contribution was to ensure the robustness of the algorithm w.r.t. the feedback errors that the user might make. Which happens more often than not in practice, especially at the initial phase of the interaction, when all the behaviours are far from the expected solution
Lee, Siu-keung, i 李少強. "Reinforcement learning for intelligent assembly automation". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31244397.
Pełny tekst źródłaTebbifakhr, Amirhossein. "Machine Translation For Machines". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.
Pełny tekst źródłaYang, Zhaoyuan Yang. "Adversarial Reinforcement Learning for Control System Design: A Deep Reinforcement Learning Approach". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152411491981452.
Pełny tekst źródłaScholz, Jonathan. "Physics-based reinforcement learning for autonomous manipulation". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54366.
Pełny tekst źródłaCleland, Andrew Lewis. "Bounding Box Improvement with Reinforcement Learning". PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4438.
Pełny tekst źródłaPiano, Francesco. "Deep Reinforcement Learning con PyTorch". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25340/.
Pełny tekst źródłaSuggs, Sterling. "Reinforcement Learning with Auxiliary Memory". BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/9028.
Pełny tekst źródłaKsiążki na temat "Reinforcement learning (Machine learning)"
S, Sutton Richard, red. Reinforcement learning. Boston: Kluwer Academic Publishers, 1992.
Znajdź pełny tekst źródłaSutton, Richard S. Reinforcement Learning. Boston, MA: Springer US, 1992.
Znajdź pełny tekst źródłaPack, Kaelbling Leslie, red. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.
Znajdź pełny tekst źródłaSzepesvári, Csaba. Algorithms for reinforcement learning. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.
Znajdź pełny tekst źródłaKaelbling, Leslie Pack. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.
Znajdź pełny tekst źródłaSutton, Richard S. Reinforcement learning: An introduction. Cambridge, Mass: MIT Press, 1998.
Znajdź pełny tekst źródłaKulkarni, Parag. Reinforcement and systemic machine learning for decision making. Hoboken, NJ: John Wiley & Sons, 2012.
Znajdź pełny tekst źródłaKulkarni, Parag. Reinforcement and Systemic Machine Learning for Decision Making. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118266502.
Pełny tekst źródłaWhiteson, Shimon. Adaptive representations for reinforcement learning. Berlin: Springer Verlag, 2010.
Znajdź pełny tekst źródłaIWLCS 2006 (2006 Seattle, Wash.). Learning classifier systems: 10th international workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th international workshop, IWLCS 2007, London, UK, July 8, 2007 : revised selected papers. Berlin: Springer, 2008.
Znajdź pełny tekst źródłaCzęści książek na temat "Reinforcement learning (Machine learning)"
Kalita, Jugal. "Reinforcement Learning". W Machine Learning, 193–230. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003002611-5.
Pełny tekst źródłaZhou, Zhi-Hua. "Reinforcement Learning". W Machine Learning, 399–430. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_16.
Pełny tekst źródłaGeetha, T. V., i S. Sendhilkumar. "Reinforcement Learning". W Machine Learning, 271–94. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-11.
Pełny tekst źródłaJo, Taeho. "Reinforcement Learning". W Machine Learning Foundations, 359–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65900-4_16.
Pełny tekst źródłaBuhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang i in. "Reinforcement Learning". W Encyclopedia of Machine Learning, 849–51. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_714.
Pełny tekst źródłaKubat, Miroslav. "Reinforcement Learning". W An Introduction to Machine Learning, 277–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20010-1_14.
Pełny tekst źródłaKubat, Miroslav. "Reinforcement Learning". W An Introduction to Machine Learning, 331–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63913-0_17.
Pełny tekst źródłaLabaca Castro, Raphael. "Reinforcement Learning". W Machine Learning under Malware Attack, 51–60. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40442-0_6.
Pełny tekst źródłaCoqueret, Guillaume, i Tony Guida. "Reinforcement learning". W Machine Learning for Factor Investing, 257–72. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003121596-20.
Pełny tekst źródłaNorris, Donald J. "Reinforcement learning". W Machine Learning with the Raspberry Pi, 501–53. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5174-4_9.
Pełny tekst źródłaStreszczenia konferencji na temat "Reinforcement learning (Machine learning)"
"PREDICTION FOR CONTROL DELAY ON REINFORCEMENT LEARNING". W Special Session on Machine Learning. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003883405790586.
Pełny tekst źródłaFu, Cailing, Jochen Stollenwerk i Carlo Holly. "Reinforcement learning for guiding optimization processes in optical design". W Applications of Machine Learning 2022, redaktorzy Michael E. Zelinski, Tarek M. Taha i Jonathan Howe. SPIE, 2022. http://dx.doi.org/10.1117/12.2632425.
Pełny tekst źródłaTittaferrante, Andrew, i Abdulsalam Yassine. "Benchmarking Offline Reinforcement Learning". W 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00044.
Pełny tekst źródłaBernstein, Alexander V., i E. V. Burnaev. "Reinforcement learning in computer vision". W Tenth International Conference on Machine Vision (ICMV 2017), redaktorzy Jianhong Zhou, Petia Radeva, Dmitry Nikolaev i Antanas Verikas. SPIE, 2018. http://dx.doi.org/10.1117/12.2309945.
Pełny tekst źródłaNatarajan, Sriraam, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting i Jude Shavlik. "Multi-Agent Inverse Reinforcement Learning". W 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.65.
Pełny tekst źródłaXue, Jianyong, i Frédéric Alexandre. "Developmental Modular Reinforcement Learning". W ESANN 2022 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2022. http://dx.doi.org/10.14428/esann/2022.es2022-19.
Pełny tekst źródłaUrmanov, Marat, Madina Alimanova i Askar Nurkey. "Training Unity Machine Learning Agents using reinforcement learning method". W 2019 15th International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2019. http://dx.doi.org/10.1109/icecco48375.2019.9043194.
Pełny tekst źródłaJin, Zhuo-Jun, Hui Qian i Miao-Liang Zhu. "Gaussian processes in inverse reinforcement learning". W 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5581063.
Pełny tekst źródłaArques Corrales, Pilar, i Fidel Aznar Gregori. "Swarm AGV Optimization Using Deep Reinforcement Learning". W MLMI '20: 2020 The 3rd International Conference on Machine Learning and Machine Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3426826.3426839.
Pełny tekst źródłaLeopold, T., G. Kern-Isberner i G. Peters. "Combining Reinforcement Learning and Belief Revision - A Learning System for Active Vision". W British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.48.
Pełny tekst źródłaRaporty organizacyjne na temat "Reinforcement learning (Machine learning)"
Singh, Satinder, Andrew G. Barto i Nuttapong Chentanez. Intrinsically Motivated Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2005. http://dx.doi.org/10.21236/ada440280.
Pełny tekst źródłaGhavamzadeh, Mohammad, i Sridhar Mahadevan. Hierarchical Multiagent Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2004. http://dx.doi.org/10.21236/ada440418.
Pełny tekst źródłaHarmon, Mance E., i Stephanie S. Harmon. Reinforcement Learning: A Tutorial. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1997. http://dx.doi.org/10.21236/ada323194.
Pełny tekst źródłaTadepalli, Prasad, i Alan Fern. Partial Planning Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2012. http://dx.doi.org/10.21236/ada574717.
Pełny tekst źródłaVesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), styczeń 2019. http://dx.doi.org/10.2172/1492563.
Pełny tekst źródłaValiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1993. http://dx.doi.org/10.21236/ada283386.
Pełny tekst źródłaChase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, kwiecień 1990. http://dx.doi.org/10.21236/ada223732.
Pełny tekst źródłaGhavamzadeh, Mohammad, i Sridhar Mahadevan. Hierarchical Average Reward Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2003. http://dx.doi.org/10.21236/ada445728.
Pełny tekst źródłaJohnson, Daniel W. Drive-Reinforcement Learning System Applications. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1992. http://dx.doi.org/10.21236/ada264514.
Pełny tekst źródłaKagie, Matthew J., i Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), sierpień 2016. http://dx.doi.org/10.2172/1561828.
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