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Дисертації з теми "Reinforcement Learning in Testing"

Автор: Grafiati
Опубліковано: 7 червня 2025
Оновлено: 2 серпня 2025

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1

ROMDHANA, ANDREA. "Deep Reinforcement Learning Driven Applications Testing." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1105635.

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Анотація:
Applications have become indispensable in our lives, and ensuring their correctness is now a critical issue. Automatic system test case generation can significantly improve the testing process for these applications, which has recently motivated researchers to work on this problem, defining various approaches. However, most state-of-the-art approaches automatically generate test cases leveraging symbolic execution or random exploration techniques. This led to techniques that lose efficiency when dealing with an increasing number of program constraints and become inapplicable when conditions ar
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2

Silguero, Russell V. "Do contingency-conflicting elements drop out of equivalence classes? Re-testing Sidman's (2000) theory." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc848078/.

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Анотація:
Sidman's (2000) theory of stimulus equivalence states that all positive elements in a reinforcement contingency enter an equivalence class. The theory also states that if an element from an equivalence class conflicts with a programmed reinforcement contingency, the conflicting element will drop out of the equivalence class. Minster et al. (2006) found evidence suggesting that a conflicting element does not drop out of an equivalence class. In an effort to explain maintained accuracy on programmed reinforcement contingencies, the authors seem to suggest that participants will behave in accorda
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3

Fakih, Saif. "A learning approach to obtain efficient testing strategies in medical diagnosis." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000309.

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4

Koppula, Sreedevi. "Automated GUI Tests Generation for Android Apps Using Q-learning." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984181/.

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Анотація:
Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is
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5

Izquierdo, Ayala Pablo. "Learning comparison: Reinforcement Learning vs Inverse Reinforcement Learning : How well does inverse reinforcement learning perform in simple markov decision processes in comparison to reinforcement learning?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259371.

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Анотація:
This research project elaborates a qualitative comparison between two different learning approaches, Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) over the Gridworld Markov Decision Process. The interest focus will be set on the second learning paradigm, IRL, as it is considered to be relatively new and little work has been developed in this field of study. As observed, RL outperforms IRL, obtaining a correct solution in all the different scenarios studied. However, the behaviour of the IRL algorithms can be improved and this will be shown and analyzed as part of the sco
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6

Seymour, B. J. "Aversive reinforcement learning." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/800107/.

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Анотація:
We hypothesise that human aversive learning can be described algorithmically by Reinforcement Learning models. Our first experiment uses a second-order conditioning design to study sequential outcome prediction. We show that aversive prediction errors are expressed robustly in the ventral striatum, supporting the validity of temporal difference algorithms (as in reward learning), and suggesting a putative critical area for appetitive-aversive interactions. With this in mind, the second experiment explores the nature of pain relief, which as expounded in theories of motivational opponency, is r
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7

Player, John Roger. "Dynamic testing of rock reinforcement systems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/58684.

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Анотація:
The research consisted of a design, construction, commissioning and comprehensive use of a Dynamic Test Facility for the testing of full scale reinforcement systems. Laboratory testing was undertaken in double embedment configuration under axial load. The energy balance for 74 tests had an average error of ± 2.7kJ measured at impact velocities ranging from 3 to 8m/sec, with total input energies in the range 20 to 96kJ. The results from testing a wide range of reinforcement systems are presented and recommendations made for their application to dynamic rock failure.
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8

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

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Анотація:
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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9

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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10

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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11

Cortesi, Daniele. "Reinforcement Learning in Rogue." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16138/.

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Анотація:
In this work we use Reinforcement Learning to play the famous Rogue, a dungeon-crawler videogame father of the rogue-like genre. By employing different algorithms we substantially improve on the results obtained in previous work, addressing and solving the problems that were arisen. We then devise and perform new experiments to test the limits of our own solution and encounter additional and unexpected issues in the process. In one of the investigated scenario we clearly see that our approach is not yet enough to even perform better than a random agent and propose ideas for future works.
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12

Girgin, Sertan. "Abstraction In Reinforcement Learning." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608257/index.pdf.

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Анотація:
Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment. Generally, the problem to be solved contains subtasks that repeat at different regions of the state space. Without any guidance an agent has to learn the solutions of all subtask instances independently, which degrades the learning performance. In this thesis, we propose two approaches to build connections between different regions of the search space leading to better utilization of gained experience and accelerate learning is proposed. In the fir
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13

Suay, Halit Bener. "Reinforcement Learning from Demonstration." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/173.

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Анотація:
Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent's own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a human
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14

Gao, Yang. "Argumentation accelerated reinforcement learning." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/26603.

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Анотація:
Reinforcement Learning (RL) is a popular statistical Artificial Intelligence (AI) technique for building autonomous agents, but it suffers from the curse of dimensionality: the computational requirement for obtaining the optimal policies grows exponentially with the size of the state space. Integrating heuristics into RL has proven to be an effective approach to combat this curse, but deriving high-quality heuristics from people's (typically conflicting) domain knowledge is challenging, yet it received little research attention. Argumentation theory is a logic-based AI technique well-known for
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15

Alexander, John W. "Transfer in reinforcement learning." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227908.

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Анотація:
The problem of developing skill repertoires autonomously in robotics and artificial intelligence is becoming ever more pressing. Currently, the issues of how to apply prior knowledge to new situations and which knowledge to apply have not been sufficiently studied. We present a transfer setting where a reinforcement learning agent faces multiple problem solving tasks drawn from an unknown generative process, where each task has similar dynamics. The task dynamics are changed by varying in the transition function between states. The tasks are presented sequentially with the latest task presente
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16

Leslie, David S. "Reinforcement learning in games." Thesis, University of Bristol, 2004. http://hdl.handle.net/1983/420b3f4b-a8b3-4a65-be23-6d21f6785364.

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17

Schneider, Markus. "Reinforcement Learning für Laufroboter." [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:bsz:747-opus-344.

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18

Wülfing, Jan [Verfasser], and Martin [Akademischer Betreuer] Riedmiller. "Stable deep reinforcement learning." Freiburg : Universität, 2019. http://d-nb.info/1204826188/34.

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19

Zhang, Jingwei [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Learning navigation policies with deep reinforcement learning." Freiburg : Universität, 2021. http://d-nb.info/1235325571/34.

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20

Rottmann, Axel [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Approaches to online reinforcement learning for miniature airships = Online Reinforcement Learning Verfahren für Miniaturluftschiffe." Freiburg : Universität, 2012. http://d-nb.info/1123473560/34.

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21

Palumbo, Nicolino. "Accelerated corrosion testing of steel reinforcement in concrete." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=60681.

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Анотація:
In the last few decades, there has been an increasing worldwide problem of deterioration of reinforced concrete structures, caused primarily by the corrosion of the steel reinforcement embedded within the concrete. Several factors can influence the corrosion process in different types of inservice structures. This thesis reviews the basic principles of the reinforcement corrosion. Various protection and rehabilitation schemes that can be undertaken in the repair of deteriorated concrete structures are presented. In particular, three specific types of structures in the Montreal region which hav
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22

Blixt, Rikard, and Anders Ye. "Reinforcement learning AI to Hive." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134908.

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Анотація:
This report is about the game Hive, which is a very unique board game. Firstly we cover what Hive is, and then later details on our implementations of it, which issues we ran into during the implementation and how we solved those issues. Also we attempted to make an AI and by using reinforcement learning teaching it to become good at playing Hive. More precisely we used two AI that has no knowledge of Hive other than game rules. This however turned out to be impossible within reasonable timeframe, our estimations is that it would have to run on an upper-end home computer for at least 140 years
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23

Borgstrand, Richard, and Patrik Servin. "Reinforcement Learning AI till Fightingspel." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3113.

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Utförandet av projektet har varit att implementera två stycken fightingspels Artificiell Intelligens (kommer att förkortas AI). En oadaptiv och mer deterministisk AI och en adaptiv dynamisk AI som använder reinforcement learning. Detta har utförts med att skripta beteendet av AI:n i en gratis 2D fightingsspels motor som heter ”MUGEN”. AI:n använder sig utav skriptade sekvenser som utförs med MUGEN’s egna trigger och state system. Detta system kollar om de skriptade specifierade kraven är uppfyllda för AI:n att ska ”trigga”, utföra den bestämda handlingen. Den mer statiska AI:n har blivit uppby
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24

Arnekvist, Isac. "Reinforcement learning for robotic manipulation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216386.

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Анотація:
Reinforcement learning was recently successfully used for real-world robotic manipulation tasks, without the need for human demonstration, usinga normalized advantage function-algorithm (NAF). Limitations on the shape of the advantage function however poses doubts to what kind of policies can be learned using this method. For similar tasks, convolutional neural networks have been used for pose estimation from images taken with fixed position cameras. For some applications however, this might not be a valid assumption. It was also shown that the quality of policies for robotic tasks severely de
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25

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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Анотація:
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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26

Cleland, Benjamin George. "Reinforcement Learning for Racecar Control." The University of Waikato, 2006. http://hdl.handle.net/10289/2507.

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Анотація:
This thesis investigates the use of reinforcement learning to learn to drive a racecar in the simulated environment of the Robot Automobile Racing Simulator. Real-life race driving is known to be difficult for humans, and expert human drivers use complex sequences of actions. There are a large number of variables, some of which change stochastically and all of which may affect the outcome. This makes driving a promising domain for testing and developing Machine Learning techniques that have the potential to be robust enough to work in the real world. Therefore the principles of the algorithm
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27

Kim, Min Sub Computer Science &amp Engineering Faculty of Engineering UNSW. "Reinforcement learning by incremental patching." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/39716.

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Анотація:
This thesis investigates how an autonomous reinforcement learning agent can improve on an approximate solution by augmenting it with a small patch, which overrides the approximate solution at certain states of the problem. In reinforcement learning, many approximate solutions are smaller and easier to produce than ???flat??? solutions that maintain distinct parameters for each fully enumerated state, but the best solution within the constraints of the approximation may fall well short of global optimality. This thesis proposes that the remaining gap to global optimality can be efficiently mini
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28

Patrascu, Relu-Eugen. "Adaptive exploration in reinforcement learning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ35921.pdf.

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29

Li, Jingxian. "Reinforcement learning using sensorimotor traces." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45590.

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Анотація:
The skilled motions of humans and animals are the result of learning good solutions to difficult sensorimotor control problems. This thesis explores new models for using reinforcement learning to acquire motion skills, with potential applications to computer animation and robotics. Reinforcement learning offers a principled methodology for tackling control problems. However, it is difficult to apply in high-dimensional settings, such as the ones that we wish to explore, where the body can have many degrees of freedom, the environment can have significant complexity, and there can be fur
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30

Rummery, Gavin Adrian. "Problem solving with reinforcement learning." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363828.

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31

McCabe, Jonathan Aiden. "Reinforcement learning in virtual reality." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608852.

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32

Budhraja, Karan Kumar. "Neuroevolution Based Inverse Reinforcement Learning." Thesis, University of Maryland, Baltimore County, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10140581.

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Анотація:
<p> Motivated by such learning in nature, the problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One of the approaches to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task. Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy simi
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33

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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Анотація:
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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34

Kozlova, Olga. "Hierarchical and factored reinforcement learning." Paris 6, 2010. http://www.theses.fr/2010PA066196.

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Анотація:
Les méthodes d'apprentissage par renforcement factorisé et hiérarchique (HFRL) sont basées sur le formalisme des processus de décision markoviens factorisées (FMDP) et les MDP hiérarchiques (HMDP). Dans cette thèse, nous proposons une méthode de HFRL qui utilise les approches d’apprentissage par renforcement indirect et le formalisme des options pour résoudre les problèmes de prise de décision dans les environnements dynamiques sans connaissance a priori de la structure du problème. Dans la première contribution de cette thèse, nous montrons comment modéliser les problèmes où certaines combina
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35

Blows, Curtly. "Reinforcement learning for telescope optimisation." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31352.

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Анотація:
Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement learning for telescope target selection and scheduling in astronomy with the hope of effectively mimicking the choices made by professional astronomers. This is relevant as next-generation astronomy surveys will require near realtime decision making in response to high-speed transient discoveries. We
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36

Stigenberg, Jakob. "Scheduling using Deep Reinforcement Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284506.

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Анотація:
As radio networks have continued to evolve in recent decades, so have theircomplexity and the difficulty in efficiently utilizing the available resources. Ina cellular network, the scheduler controls the allocation of time, frequencyand spatial resources to users in both uplink and downlink directions. Thescheduler is therefore a key component in terms of efficient usage of networkresources. Although the scope and characteristics of available resources forschedulers are well defined in network standards, e.g. Long-Term Evolutionor New Radio, its real implementation is not. Most previous work f
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37

Jesu, Alberto. "Reinforcement learning over encrypted data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23257/.

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Анотація:
Reinforcement learning is a particular paradigm of machine learning that, recently, has proved times and times again to be a very effective and powerful approach. On the other hand, cryptography usually takes the opposite direction. While machine learning aims at analyzing data, cryptography aims at maintaining its privacy by hiding such data. However, the two techniques can be jointly used to create privacy preserving models, able to make inferences on the data without leaking sensitive information. Despite the numerous amount of studies performed on machine learning and cryptography, reinfor
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38

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

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Анотація:
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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39

Liu, Chong. "Reinforcement learning with time perception." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/reinforcement-learning-with-time-perception(a03580bd-2dd6-4172-a061-90e8ac3022b8).html.

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Анотація:
Classical value estimation reinforcement learning algorithms do not perform very well in dynamic environments. On the other hand, the reinforcement learning of animals is quite flexible: they can adapt to dynamic environments very quickly and deal with noisy inputs very effectively. One feature that may contribute to animals' good performance in dynamic environments is that they learn and perceive the time to reward. In this research, we attempt to learn and perceive the time to reward and explore situations where the learned time information can be used to improve the performance of the learn
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40

Tluk, von Toschanowitz Katharina. "Relevance determination in reinforcement learning." Tönning Lübeck Marburg Der Andere Verl, 2009. http://d-nb.info/993341128/04.

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41

Bonneau, Maxime. "Reinforcement Learning for 5G Handover." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140816.

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Анотація:
The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs.  In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to
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42

Ovidiu, Chelcea Vlad, and Björn Ståhl. "Deep Reinforcement Learning for Snake." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239362.

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Анотація:
The world has recently seen a large increase in both research and development and layman use of machine learning. Machine learning has a broad application domain, e.g, in marketing, production and finance. Although these applications have a predetermined set of rules or goals, this project deals with another aspect of machine learning which is general intelligence. During the course of the project a non-human player (known as agent) will learn how to play the game SNAKE without any outside influence or knowledge of the environment dynamics. After having the agent train for 66 hours and almost
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43

Edlund, Joar, and Jack Jönsson. "Reinforcement Learning for Video Games." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239363.

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We present an implementation of a specific type of deep reinforcement learning algorithm known as deep Qlearning. With a Convolutional Neural Network (CNN) combined with our Q-learning algorithm, we trained an agent to play the game of Snake. The input to the CNN is the raw pixel values from the Snake environment and the output is a value function which estimates future rewards for different actions. We implemented the Q-learning algorithm on a grid based and a pixel based representation of the Snake environment and found that the algorithm can perform at human level on smaller grid based repr
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44

Magnusson, Björn, and Måns Forslund. "SAFE AND EFFICIENT REINFORCEMENT LEARNING." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-76588.

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Pre-programming a robot may be efficient to some extent, but since a human has code the robot it will only be as efficient as the programming. The problem can solved by using machine learning, which lets the robot learn the most efficient way by itself. This thesis is continuation of a previous work that covered the development of the framework ​Safe-To-Explore-State-Spaces​ (STESS) for safe robot manipulation. This thesis evaluates the efficiency of the ​Q-Learning with normalized advantage function ​ (NAF), a deep reinforcement learning algorithm, when integrated with the safety framework ST
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45

Liu, Bai S. M. Massachusetts Institute of Technology. "Reinforcement learning in network control." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122414.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-91).<br>With the rapid growth of information technology, network systems have become increasingly complex. In particular, designing network control policies requires knowledge of underlying network dynamics, which are often unknown, and need to be learned. Existing reinforcement learning methods such as Q-Learning, Actor-Critic, etc. are heuristic and do not offer performance guarantees. In contrast, mode
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46

Garcelon, Evrard. "Constrained Exploration in Reinforcement Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG007.

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Une application majeure de l'apprentissage machine automatisée est la personnalisation des différents contenus recommandé à différents utilisateurs. Généralement, les algorithmes étant à la base de ces systèmes sont dit supervisé. C'est-à-dire que les données utilisées lors de la phase d'apprentissage sont supposées provenir de la même distribution. Cependant, ces données sont générées par des interactions entre un utilisateur et ces mêmes algorithmes. Ainsi, les recommandations pour un utilisateur à un instant t peuvent modifier l'ensemble des recommandations pertinentes à un instant ultérieu
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47

Wei, Ermo. "Learning to Play Cooperative Games via Reinforcement Learning." Thesis, George Mason University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420351.

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<p> Being able to accomplish tasks with multiple learners through learning has long been a goal of the multiagent systems and machine learning communities. One of the main approaches people have taken is reinforcement learning, but due to certain conditions and restrictions, applying reinforcement learning in a multiagent setting has not achieved the same level of success when compared to its single agent counterparts. </p><p> This thesis aims to make coordination better for agents in cooperative games by improving on reinforcement learning algorithms in several ways. I begin by examining ce
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48

Stachenfeld, Kimberly. "Learning Neural Representations that Support Efficient Reinforcement Learning." Thesis, Princeton University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10824319.

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<p>RL has been transformative for neuroscience by providing a normative anchor for interpreting neural and behavioral data. End-to-end RL methods have scored impressive victories with minimal compromises in autonomy, hand-engineering, and generality. The cost of this minimalism in practice is that model-free RL methods are slow to learn and generalize poorly. Humans and animals exhibit substantially improved flexibility and generalize learned information rapidly to new environment by learning invariants of the environment and features of the environment that support fast learning rapid transfe
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49

Furniss, Jonathan P. "Testing and evaluation of GRP rockbolts for tunnel reinforcement." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395497.

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50

Le, Piane Fabio. "Training cognitivo adattativo mediante Reinforcement Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17289/.

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La sclerosi multipla (SM) è una malattia autoimmune che colpisce il sistema nervoso centrale causando varie alterazioni organiche e funzionali. In particolare, una rilevante percentuale di pazienti sviluppa deficit in differenti domini cognitivi. Per limitare la progressione di tali deficit, team specialistici hanno ideato dei protocolli per la riabilitazione cognitiva. Per effettuare le sedute di riabilitazione, i pazienti devono recarsi in cliniche specializzate, necessitando dell'assistenza di personale qualificato e svolgendo gli esercizi tramite scrittura su carta. In seguito, si è inizi
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