Bibliografie tematiche / Reinforcement learning (Machine learning)

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Letteratura scientifica selezionata sul tema "Reinforcement learning (Machine learning)"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 7 settembre 2023

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Articoli di riviste sul tema "Reinforcement learning (Machine learning)"

Ishii, Shin, and Wako Yoshida. "Part 4: Reinforcement learning: Machine learning and natural learning." New Generation Computing 24, no. 3 (September 2006): 325–50. http://dx.doi.org/10.1007/bf03037338.

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Wang, Zizhuang. "Temporal-Related Convolutional-Restricted-Boltzmann-Machine Capable of Learning Relational Order via Reinforcement Learning Procedure." International Journal of Machine Learning and Computing 7, no. 1 (February 2017): 1–8. http://dx.doi.org/10.18178/ijmlc.2017.7.1.610.

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Butlin, Patrick. "Machine Learning, Functions and Goals." Croatian journal of philosophy 22, no. 66 (December 27, 2022): 351–70. http://dx.doi.org/10.52685/cjp.22.66.5.

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Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs on subsequent inputs to

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Martín-Guerrero, José D., and Lucas Lamata. "Reinforcement Learning and Physics." Applied Sciences 11, no. 18 (September 16, 2021): 8589. http://dx.doi.org/10.3390/app11188589.

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Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of

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Liu, Yicen, Yu Lu, Xi Li, Wenxin Qiao, Zhiwei Li, and Donghao Zhao. "SFC Embedding Meets Machine Learning: Deep Reinforcement Learning Approaches." IEEE Communications Letters 25, no. 6 (June 2021): 1926–30. http://dx.doi.org/10.1109/lcomm.2021.3061991.

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Popkov, Yuri S., Yuri A. Dubnov, and Alexey Yu Popkov. "Reinforcement Procedure for Randomized Machine Learning." Mathematics 11, no. 17 (August 23, 2023): 3651. http://dx.doi.org/10.3390/math11173651.

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This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm

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Crawford, Daniel, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya Ronagh. "Reinforcement learning using quantum Boltzmann machines." Quantum Information and Computation 18, no. 1&2 (February 2018): 51–74. http://dx.doi.org/10.26421/qic18.1-2-3.

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We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations

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Lamata, Lucas. "Quantum Reinforcement Learning with Quantum Photonics." Photonics 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/photonics8020033.

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Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum c

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Sahu, Santosh Kumar, Anil Mokhade, and 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, no. 3 (February 2, 2023): 1956. http://dx.doi.org/10.3390/app13031956.

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Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate

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Fang, Qiang, Wenzhuo Zhang, and Xitong Wang. "Visual Navigation Using Inverse Reinforcement Learning and an Extreme Learning Machine." Electronics 10, no. 16 (August 18, 2021): 1997. http://dx.doi.org/10.3390/electronics10161997.

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In this paper, we focus on the challenges of training efficiency, the designation of reward functions, and generalization in reinforcement learning for visual navigation and propose a regularized extreme learning machine-based inverse reinforcement learning approach (RELM-IRL) to improve the navigation performance. Our contributions are mainly three-fold: First, a framework combining extreme learning machine with inverse reinforcement learning is presented. This framework can improve the sample efficiency and obtain the reward function directly from the image information observed by the agent

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Tesi sul tema "Reinforcement learning (Machine learning)"

Hengst, Bernhard Computer Science &amp 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.

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This thesis addresses the open problem of automatically discovering hierarchical structure in reinforcement learning. Current algorithms for reinforcement learning fail to scale as problems become more complex. Many complex environments empirically exhibit hierarchy and can be modeled as interrelated subsystems, each in turn with hierarchic structure. Subsystems are often repetitive in time and space, meaning that they reoccur as components of different tasks or occur multiple times in different circumstances in the environment. A learning agent may sometimes scale to larger problems if it suc

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Tabell, Johnsson Marco, and 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.

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Akrour, Riad. "Robust Preference Learning-based Reinforcement Learning." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.

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Les contributions de la thèse sont centrées sur la prise de décisions séquentielles et plus spécialement sur l'Apprentissage par Renforcement (AR). Prenant sa source de l'apprentissage statistique au même titre que l'apprentissage supervisé et non-supervisé, l'AR a gagné en popularité ces deux dernières décennies en raisons de percées aussi bien applicatives que théoriques. L'AR suppose que l'agent (apprenant) ainsi que son environnement suivent un processus de décision stochastique Markovien sur un espace d'états et d'actions. Le processus est dit de décision parce que l'agent est appelé à ch

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Lee, Siu-keung, and 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.

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Tebbifakhr, Amirhossein. "Machine Translation For Machines." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320504.

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Traditionally, Machine Translation (MT) systems are developed by targeting fluency (i.e. output grammaticality) and adequacy (i.e. semantic equivalence with the source text) criteria that reflect the needs of human end-users. However, recent advancements in Natural Language Processing (NLP) and the introduction of NLP tools in commercial services have opened new opportunities for MT. A particularly relevant one is related to the application of NLP technologies in low-resource language settings, for which the paucity of training data reduces the possibility to train reliable services. In this s

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Yang, 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.

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Scholz, Jonathan. "Physics-based reinforcement learning for autonomous manipulation." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54366.

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With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically in the past decade, with applications ranging from house cleaning to food service to health care. To date, the majority of the planning and control machinery for these systems are carefully designed by human engineers. A large portion of this effort goes into selecting the appropriate models and control techniques for each application, and these skills take years to master. Relieving the burden on human experts is therefo

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Cleland, Andrew Lewis. "Bounding Box Improvement with Reinforcement Learning." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4438.

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In this thesis, I explore a reinforcement learning technique for improving bounding box localizations of objects in images. The model takes as input a bounding box already known to overlap an object and aims to improve the fit of the box through a series of transformations that shift the location of the box by translation, or change its size or aspect ratio. Over the course of these actions, the model adapts to new information extracted from the image. This active localization approach contrasts with existing bounding-box regression methods, which extract information from the image only once.

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Piano, Francesco. "Deep Reinforcement Learning con PyTorch." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25340/.

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Il Reinforcement Learning è un campo di ricerca del Machine Learning in cui la risoluzione di problemi da parte di un agente avviene scegliendo l’azione più idonea da eseguire attraverso un processo di apprendimento iterativo, in un ambiente dinamico che lo incentiva tramite ricompense. Il Deep Learning, anch’esso approccio del Machine Learning, sfruttando una rete neurale artificiale è in grado di applicare metodi di apprendimento per rappresentazione allo scopo di ottenere una struttura dei dati più idonea ad essere elaborata. Solo recentemente il Deep Reinforcement Learning, creato

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Suggs, Sterling. "Reinforcement Learning with Auxiliary Memory." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/9028.

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Deep reinforcement learning algorithms typically require vast amounts of data to train to a useful level of performance. Each time new data is encountered, the network must inefficiently update all of its parameters. Auxiliary memory units can help deep neural networks train more efficiently by separating computation from storage, and providing a means to rapidly store and retrieve precise information. We present four deep reinforcement learning models augmented with external memory, and benchmark their performance on ten tasks from the Arcade Learning Environment. Our discussion and insights

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Più fonti

Libri sul tema "Reinforcement learning (Machine learning)"

S, Sutton Richard, ed. Reinforcement learning. Boston: Kluwer Academic Publishers, 1992.

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Sutton, Richard S. Reinforcement Learning. Boston, MA: Springer US, 1992.

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Pack, Kaelbling Leslie, ed. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.

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Szepesvári, Csaba. Algorithms for reinforcement learning. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

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Kaelbling, Leslie Pack. Recent advances in reinforcement learning. Boston: Kluwer Academic, 1996.

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Sutton, Richard S. Reinforcement learning: An introduction. Cambridge, Mass: MIT Press, 1998.

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Kulkarni, Parag. Reinforcement and systemic machine learning for decision making. Hoboken, NJ: John Wiley & Sons, 2012.

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Kulkarni, Parag. Reinforcement and Systemic Machine Learning for Decision Making. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118266502.

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Whiteson, Shimon. Adaptive representations for reinforcement learning. Berlin: Springer Verlag, 2010.

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IWLCS 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.

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Capitoli di libri sul tema "Reinforcement learning (Machine learning)"

Kalita, Jugal. "Reinforcement Learning." In Machine Learning, 193–230. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003002611-5.

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Zhou, Zhi-Hua. "Reinforcement Learning." In Machine Learning, 399–430. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_16.

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Geetha, T. V., and S. Sendhilkumar. "Reinforcement Learning." In Machine Learning, 271–94. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003290100-11.

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Jo, Taeho. "Reinforcement Learning." In Machine Learning Foundations, 359–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65900-4_16.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Reinforcement Learning." In Encyclopedia of Machine Learning, 849–51. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_714.

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Kubat, Miroslav. "Reinforcement Learning." In An Introduction to Machine Learning, 277–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20010-1_14.

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Kubat, Miroslav. "Reinforcement Learning." In An Introduction to Machine Learning, 331–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63913-0_17.

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Labaca Castro, Raphael. "Reinforcement Learning." In Machine Learning under Malware Attack, 51–60. Wiesbaden: Springer Fachmedien Wiesbaden, 2023. http://dx.doi.org/10.1007/978-3-658-40442-0_6.

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Coqueret, Guillaume, and Tony Guida. "Reinforcement learning." In Machine Learning for Factor Investing, 257–72. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003121596-20.

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Norris, Donald J. "Reinforcement learning." In Machine Learning with the Raspberry Pi, 501–53. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5174-4_9.

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Atti di convegni sul tema "Reinforcement learning (Machine learning)"

"PREDICTION FOR CONTROL DELAY ON REINFORCEMENT LEARNING." In Special Session on Machine Learning. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003883405790586.

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Fu, Cailing, Jochen Stollenwerk, and Carlo Holly. "Reinforcement learning for guiding optimization processes in optical design." In Applications of Machine Learning 2022, edited by Michael E. Zelinski, Tarek M. Taha, and Jonathan Howe. SPIE, 2022. http://dx.doi.org/10.1117/12.2632425.

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Tittaferrante, Andrew, and Abdulsalam Yassine. "Benchmarking Offline Reinforcement Learning." In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00044.

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Bernstein, Alexander V., and E. V. Burnaev. "Reinforcement learning in computer vision." In Tenth International Conference on Machine Vision (ICMV 2017), edited by Jianhong Zhou, Petia Radeva, Dmitry Nikolaev, and Antanas Verikas. SPIE, 2018. http://dx.doi.org/10.1117/12.2309945.

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Natarajan, Sriraam, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, and Jude Shavlik. "Multi-Agent Inverse Reinforcement Learning." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.65.

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Xue, Jianyong, and Frédéric Alexandre. "Developmental Modular Reinforcement Learning." In 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.

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Urmanov, Marat, Madina Alimanova, and Askar Nurkey. "Training Unity Machine Learning Agents using reinforcement learning method." In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO). IEEE, 2019. http://dx.doi.org/10.1109/icecco48375.2019.9043194.

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Jin, Zhuo-Jun, Hui Qian, and Miao-Liang Zhu. "Gaussian processes in inverse reinforcement learning." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5581063.

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Arques Corrales, Pilar, and Fidel Aznar Gregori. "Swarm AGV Optimization Using Deep Reinforcement Learning." In 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.

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Leopold, T., G. Kern-Isberner, and G. Peters. "Combining Reinforcement Learning and Belief Revision - A Learning System for Active Vision." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.48.

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Rapporti di organizzazioni sul tema "Reinforcement learning (Machine learning)"

Singh, Satinder, Andrew G. Barto, and Nuttapong Chentanez. Intrinsically Motivated Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada440280.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Multiagent Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada440418.

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Harmon, Mance E., and Stephanie S. Harmon. Reinforcement Learning: A Tutorial. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada323194.

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Tadepalli, Prasad, and Alan Fern. Partial Planning Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada574717.

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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Average Reward Reinforcement Learning. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada445728.

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Johnson, Daniel W. Drive-Reinforcement Learning System Applications. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada264514.

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Kagie, Matthew J., and Park Hays. FORTE Machine Learning. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561828.

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