Relevant bibliographies by topics / Reinforcement Theory

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Reinforcement Theory'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Reinforcement Theory"

1

Schoenfeld, William N. "“Reinforcement” in Behavior Theory." Behavior Analyst 18, no. 1 (1995): 173–85. http://dx.doi.org/10.1007/bf03392705.

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Maeda, Shin-ichi. "Theory of Reinforcement Learning." Journal of the Robotics Society of Japan 39, no. 7 (2021): 621–24. http://dx.doi.org/10.7210/jrsj.39.621.

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Dolatyar, Khalil, and Benjamin R. Walker. "Reinforcement sensitivity theory and mindfulness." Personality and Individual Differences 163 (September 2020): 110089. http://dx.doi.org/10.1016/j.paid.2020.110089.

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Corr, Philip J. "Reinforcement sensitivity theory and personality." Neuroscience & Biobehavioral Reviews 28, no. 3 (2004): 317–32. http://dx.doi.org/10.1016/j.neubiorev.2004.01.005.

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Adams, Mark A. "Reinforcement theory and behavior analysis." Behavioral Development Bulletin 9, no. 1 (2000): 3–6. http://dx.doi.org/10.1037/h0100529.

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Kruglikov, Ilja L., and Philipp E. Scherer. "General theory of skin reinforcement." PLOS ONE 12, no. 8 (2017): e0182865. http://dx.doi.org/10.1371/journal.pone.0182865.

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Villere, Maurice F., and Sandra S. Hartman. "Reinforcement Theory: A Practical Tool." Leadership & Organization Development Journal 12, no. 2 (1991): 27–31. http://dx.doi.org/10.1108/01437739110138039.

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Cho, Young Tae, Duck Young Yoon, and Kwang Hee Im. "Damage Theory for Discontinuously-Reinforced Composites Including Cracked Inhomogeneity." International Journal of Modern Physics B 17, no. 08n09 (2003): 1801–7. http://dx.doi.org/10.1142/s0217979203019691.

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In particle or short-fiber reinforced composites, cracking of the reinforcements is a significant damage mode because the cracked reinforcements lose load carrying capacity. This paper deals with an incremental damage theory of particle or short-fiber reinforced composites. The composite undergoing damage process contains intact and broken reinforcements in a matrix. To describe the load carrying capacity of the cracked reinforcement, the average stress of a cracked ellipsoidal inhomogeneity in infinite body, which was proposed in the previous paper is introduced. An incremental constitutive r
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Johnson, Judith L., Lucy M. Kim, Thorayya S. Giovannelli, and Tiffany Cagle. "Reinforcement sensitivity theory, vengeance, and forgiveness." Personality and Individual Differences 48, no. 5 (2010): 612–16. http://dx.doi.org/10.1016/j.paid.2009.12.018.

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Balzano, M., and G. Paderni. "Stochastic reinforcement problems and ergodic theory." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 121, no. 1-2 (1992): 33–53. http://dx.doi.org/10.1017/s030821050001413x.

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SynopsisWe study the asymptotic behaviour of Dirichlet problems in domains of R2 bounded by thin layers whose thickness is given by means of an assigned ergodic random function. Using a capacitary method together with ergodic theorems for additive and superadditive processes, we are able to characterise the limit problem precisely.
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Dissertations / Theses on the topic "Reinforcement Theory"

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Hutchison, Ann Mechthild. "Reinforcement sensitivity theory, personality, and senior executive performance." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/6732.

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This thesis explores the extent to which Jeffrey Gray's reinforcement sensitivity theory of personality can predict and explain the job performance of top-level executive leaders. In this study, 189 senior executives underwent a battery of psychometric, biographical and performance measures. The dimensions of approach and avoidance motivation were measured using Carver and White's (1994) 'BIS/BAS Scales', and the predictive power of these dimensions was compared to R. Hogan and Hogan's (1997) ambition and adjustment traits. Overall, a model of senior executive performance prediction was
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Eftekhari, Zahra. "Preana: Game-theory Based Prediction with Reinforcement Learning." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1552.

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We have developed a game-theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita's algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players' reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple stake-holders who have conflicting interests in economic, business, and political sciences
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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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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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Montag, Christian. "Die revidierte reinforcement sensitivity theory eine experimentell-biologische Überprüfung." Hamburg Kovač, 2008. http://d-nb.info/993570275/04.

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Mouton, Hildegarde Suzanne. "Reinforcement learning : theory, methods and application to decision support systems." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5304.

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Thesis (MSc (Applied Mathematics))--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: In this dissertation we study the machine learning subfield of Reinforcement Learning (RL). After developing a coherent background, we apply a Monte Carlo (MC) control algorithm with exploring starts (MCES), as well as an off-policy Temporal-Difference (TD) learning control algorithm, Q-learning, to a simplified version of the Weapon Assignment (WA) problem. For the MCES control algorithm, a discount parameter of τ = 1 is used. This gives very promising results when applied to 7 × 7 grids, as wel
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Keramati, Mohammadmahdi. "A homeostatic reinforcement learning theory, and its implications in cocaine addiction." Thesis, Paris 5, 2013. http://www.theses.fr/2013PA05T027/document.

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Cette thèse est composée de deux parties. Dans la première partie, nous proposons une théorie pour l'interaction entre l'apprentissage par renforcement et les processus de régulation homéostatique. En fait, la régulation efficace de l'homéostasie interne et la défendre contre les perturbations a besoin des stratégies comportementales complexes pour obtenir des ressources physiologiquement épuisés. À cet égard, il est essentiel que les processus cérébraux de régulation homéostatique et les processus d'apprentissage associatifs travaillent de concert. Nous proposons une théorie computationnelle
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Jurvelin, Olsson Mikael. "MULTI-AGENT REINFORCEMENT LEARNING WITH APPLICATION ON TRAFFIC FLOW CONTROL." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447308.

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Traffic congestion diminish driving experience and increases the CO2 emissions. With the rise of 5G and machine learning, the possibilities to reduce traffic congestion are endless. This thesis aims to study if multi-agent reinforcement learning speed recommendations on a vehicle level can reduce congestion and thus control traffic flow. This is done by simulating a highway with an obstacle on one side of the lanes, forcing all the vehicles to drive on the same lane past the obstacle, resulting in congestion. A game theory aspect of drivers not obeying the speed recommendations was implemented
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Thrailkill, Eric A. "Token reinforcement and resistance to change." DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1527.

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Interventions based on a token economy effectively reduce problematic behavior. Yet, treatment gains deteriorate once an intervention is discontinued. It is important to better understand the persistence of behavior maintained by token reinforcement in simple experimental procedures. A Pavlovian association with primary reinforcement is said to endow neutral stimuli (e.g., coins, poker chips, lights, signs, stickers, etc.) with their own function to strengthen behavior as conditioned reinforcers. Behavioral momentum theory suggests that resistance to change under conditions of disruption is th
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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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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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Lindberg, Jesper. "Simulation driven reinforcement learning : Improving synthetic enemies in flight simulators." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166593.

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This project focuses on how to implement an Artificial Intelligence (AI) -agent in a Tactical Simulator (Tacsi). Tacsi is a simulator used by Saab AB, one thing that the simulator is used for is pilot training. In this work, Tacsi will be used to simulate air to air combat. The agent uses Reinforcement Learning (RL) to be able to explore and learn how the simulator behaves. This knowledge will then be exploited when the agent tries to beat a computer-controlled synthetic enemy. The result of this study may be used to produce better synthetic enemies for pilot training. The RL-algorithm used in
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Books on the topic "Reinforcement Theory"

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Corr, Philip J., ed. The Reinforcement Sensitivity Theory of Personality. Cambridge University Press, 2008. http://dx.doi.org/10.1017/cbo9780511819384.

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International, Geotechnical Symposium on Theory and Practice of Earth Reinforcement (1988 Fukuoka Kyushu Japan). Theory and practice of earth reinforcement: Proceedings of the International Geotechnical Symposium on Theory and Practice of Earth Reinforcement, Fukuoka Kyushu, 5-7, October, 1988. A.A. Balkema, 1988.

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Gosavi, Abhijit. Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning. Springer US, 2003.

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Morillo, Carolyn R. Contingent creatures: A reward event theory of motivation and value. Littlefield Adams Books, 1995.

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Zotter, Franz. Ambisonics: A Practical 3D Audio Theory for Recording, Studio Production, Sound Reinforcement, and Virtual Reality. Springer Nature, 2019.

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Keller, Thomas. Use of fibre reinforced polymers in bridge construction. International Association for Bridge and Structural Engineering (IABSE), 2003. http://dx.doi.org/10.2749/sed007.

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&lt;p&gt;The aim of the present Structural Engineering Document, a state-of-the-art report, is to review the progress made worldwide in the use of fibre rein­forced polymers as structural components in bridges until the end of the year 2000.&lt;p&gt; Due to their advantageous material properties such as high specific strength, a large tolerance for frost and de-icing salts and, furthermore, short installation times with minimum traffic interference, fibre reinforced polymers have matured to become valuable alternative building materials for bridge structures. Today, fibre reinforced polymers a
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Walsh, Anthony. Reinforcement Sensitivity Theory. Taylor & Francis Group, 2020.

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The Reinforcement Sensitivity Theory of Personality. Cambridge University Press, 2008.

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Corr, Philip J. The Reinforcement Sensitivity Theory of Personality. Cambridge University Press, 2008.

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The reinforcement sensitivity theory of personality. Cambridge University Press, 2008.

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Book chapters on the topic "Reinforcement Theory"

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Nandy, Abhishek, and Manisha Biswas. "RL Theory and Algorithms." In Reinforcement Learning. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3285-9_2.

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Corr, Philip. "Reinforcement Sensitivity Theory." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1153.

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Corr, Philip. "Reinforcement Sensitivity Theory." In Encyclopedia of Personality and Individual Differences. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-28099-8_1153-1.

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Andrews, Anne M., Greg A. Gerhardt, Lynette C. Daws, et al. "Negative Reinforcement Theory." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_1684.

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Walsh, Anthony. "Biosocial Criminology." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-1.

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Walsh, Anthony. "The Schizophrenia Spectrum and Crime." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-10.

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Walsh, Anthony. "Attention-Deficit/Hyperactivity Disorder." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-11.

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Walsh, Anthony. "Criminal Violence." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-12.

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Walsh, Anthony. "A Question for All Ages." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-2.

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Walsh, Anthony. "Behavioral Genetics." In Reinforcement Sensitivity Theory. Routledge, 2018. http://dx.doi.org/10.4324/9780429428234-3.

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Conference papers on the topic "Reinforcement Theory"

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Li, Jinci. "Wireless, amphibious theory for reinforcement learning." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825527.

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Subramanian, Jayakumar, and Aditya Mahajan. "Renewal Monte Carlo: Renewal Theory Based Reinforcement Learning." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619180.

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Borkar, Vivek S., and Ankushkumar Jain. "Reinforcement learning, particle filters and the EM algorithm." In 2014 Information Theory and Applications Workshop (ITA). IEEE, 2014. http://dx.doi.org/10.1109/ita.2014.6804269.

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Ye, Lingjian, and Yimin Zhou. "Obstacle Avoidance with Reinforcement Learning and Adaptive Resonance Theory." In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961847.

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Chen, Chunlin, Daoyi Dong, Yu Dong, and Qiong Shi. "A Quantum Reinforcement Learning Method for Repeated Game Theory." In 2006 International Conference on Computational Intelligence and Security. IEEE, 2006. http://dx.doi.org/10.1109/iccias.2006.294092.

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Salmani, Mahsa, Foad Sohrabi, Timothy N. Davidson, and Wei Yu. "Multiple Access Binary Computation Offloading via Reinforcement Learning." In 2019 16th Canadian Workshop on Information Theory (CWIT). IEEE, 2019. http://dx.doi.org/10.1109/cwit.2019.8929930.

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Obara, Mitsuaki, Takehiro Kashiyama, and Yoshihide Sekimoto. "Deep Reinforcement Learning Approach for Train Rescheduling Utilizing Graph Theory." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622214.

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Zhifei, Shao, and Er Meng Joo. "A review of inverse reinforcement learning theory and recent advances." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256507.

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Sikora, Jan, and Renata Wagnerova. "Overview of Reinforcement Learning and its Application in Control Theory." In 2020 21th International Carpathian Control Conference (ICCC). IEEE, 2020. http://dx.doi.org/10.1109/iccc49264.2020.9257272.

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Xiangping, Meng, Pi Yuzhen, Yuan Quande, and Pan Ying. "A Study of Multiagent Reinforcement Learning based on Quantum Theory." In The Proceedings of the Multiconference on "Computational Engineering in Systems Applications". IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.313640.

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Reports on the topic "Reinforcement Theory"

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DeAnna, Dixon, and Hodo Wayne. Finite element analysis of quoin block deterioration and load transfer mechanisms in miter gates : pintle and pintle connections. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40842.

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The U.S. Army Corps of Engineers (USACE) currently operates and maintains approximately 193 commercially active lock sites with 239 locks and dams spanning nearly 12,000 miles. These networks of water channels are used to transport 600 million tons of domestic cargo, generating $405 billion in revenue annually. Nearly 60% of these structures in operation are over 50 years old and have reached design life. A failure of the miter gates could result in a major negative impact on the economy and on the ability to maintain flood control. Administrators need recommendations to better prioritize main
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