Relevant bibliographies by topics / Q-learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Q-learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Q-learning"

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Watkins, Christopher J. C. H., and Peter Dayan. "Q-learning." Machine Learning 8, no. 3-4 (1992): 279–92. http://dx.doi.org/10.1007/bf00992698.

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Clausen, C., and H. Wechsler. "Quad-Q-learning." IEEE Transactions on Neural Networks 11, no. 2 (2000): 279–94. http://dx.doi.org/10.1109/72.839000.

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ten Hagen, Stephan, and Ben Kr�se. "Neural Q-learning." Neural Computing & Applications 12, no. 2 (2003): 81–88. http://dx.doi.org/10.1007/s00521-003-0369-9.

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Wang, Yin-Hao, Tzuu-Hseng S. Li, and Chih-Jui Lin. "Backward Q-learning: The combination of Sarsa algorithm and Q-learning." Engineering Applications of Artificial Intelligence 26, no. 9 (2013): 2184–93. http://dx.doi.org/10.1016/j.engappai.2013.06.016.

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Evseenko, Alla, and Dmitrii Romannikov. "Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum." Transaction of Scientific Papers of the Novosibirsk State Technical University, no. 1-2 (August 26, 2020): 7–25. http://dx.doi.org/10.17212/2307-6879-2020-1-2-7-25.

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Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is m
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Abedalguni, Bilal. "Bat Q-learning Algorithm." Jordanian Journal of Computers and Information Technology 3, no. 1 (2017): 51. http://dx.doi.org/10.5455/jjcit.71-1480540385.

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Zhu, Rong, and Mattia Rigotti. "Self-correcting Q-learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 11185–92. http://dx.doi.org/10.1609/aaai.v35i12.17334.

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The Q-learning algorithm is known to be affected by the maximization bias, i.e. the systematic overestimation of action values, an important issue that has recently received renewed attention. Double Q-learning has been proposed as an efficient algorithm to mitigate this bias. However, this comes at the price of an underestimation of action values, in addition to increased memory requirements and a slower convergence. In this paper, we introduce a new way to address the maximization bias in the form of a "self-correcting algorithm" for approximating the maximum of an expected value. Our method
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Borkar, Vivek S., and Siddharth Chandak. "Prospect-theoretic Q-learning." Systems & Control Letters 156 (October 2021): 105009. http://dx.doi.org/10.1016/j.sysconle.2021.105009.

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Ganger, Michael, and Wei Hu. "Quantum Multiple Q-Learning." International Journal of Intelligence Science 09, no. 01 (2019): 1–22. http://dx.doi.org/10.4236/ijis.2019.91001.

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John, Indu, Chandramouli Kamanchi, and Shalabh Bhatnagar. "Generalized Speedy Q-Learning." IEEE Control Systems Letters 4, no. 3 (2020): 524–29. http://dx.doi.org/10.1109/lcsys.2020.2970555.

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Dissertations / Theses on the topic "Q-learning"

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Gaskett, Chris, and cgaskett@it jcu edu au. "Q-Learning for Robot Control." The Australian National University. Research School of Information Sciences and Engineering, 2002. http://thesis.anu.edu.au./public/adt-ANU20041108.192425.

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Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQ-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing
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Gaskett, Chris. "Q-Learning for robot control." View thesis entry in Australian Digital Theses Program, 2002. http://eprints.jcu.edu.au/623/1/gaskettthesis.pdf.

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Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQ-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing
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Laivamaa, J. (Juuso). "Reinforcement Q-Learning using OpenAI Gym." Bachelor's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201903151329.

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Abstract. Q-Learning is an off-policy algorithm for reinforcement learning, that can be used to find optimal policies in Markovian domains. This thesis is about how Q-Learning can be applied to a test environment in the OpenAI Gym toolkit. The utility of testing the algorithm on a problem case is to find out how well it performs as well proving the practical utility of the algorithm. This thesis starts off with a general overview of reinforcement learning as well as the Markov decision process, both of which are crucial in understanding the theoretical groundwork that Q-Learning is based on. A
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Del, Ben Enrico <1997&gt. "Reinforcement Learning: a Q-Learning Algorithm for High Frequency Trading." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/20411.

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The scope of this work is to test the implementation of an automated trading system based on Reinforcement Learning: a machine learning algorithm in which an intelligent agent acts to maximize its rewards given the environment around it. Indeed, given the environmental inputs and the environmental responses to the actions taken, the agent will learn how to behave in best way possible. In particular, in this work, a Q-Learning algorithm has been used to produce trading signals on the basis of high frequency data of the Limit Order Book for some selected stocks.
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Karlsson, Daniel. "Hyperparameter optimisation using Q-learning based algorithms." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-78096.

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Machine learning algorithms have many applications, both for academic and industrial purposes. Examples of applications are classification of diffraction patterns in materials science and classification of properties in chemical compounds within the pharmaceutical industry. For these algorithms to be successful they need to be optimised,  part of this is achieved by training the algorithm, but there are components of the algorithms that cannot be trained. These hyperparameters have to be tuned separately. The focus of this work was optimisation of hyperparameters in classification algorithms b
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Finnman, Peter, and Max Winberg. "Deep reinforcement learning compared with Q-table learning applied to backgammon." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186545.

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Reinforcement learning attempts to mimic how humans react to their surrounding environment by giving feedback to software agents based on the actions they take. To test the capabilities of these agents, researches have long regarded board games as a powerful tool. This thesis compares two approaches to reinforcement learning in the board game backgammon, a Q-table and a deep reinforcement network. It was determined which approach surpassed the other in terms of accuracy and convergence rate towards the perceived optimal strategy. The evaluation is performed by training the agents using the sel
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Patel, Purvag. "Improving Computer Game Bots' behavior using Q-Learning." Available to subscribers only, 2009. http://proquest.umi.com/pqdweb?did=1966544161&sid=3&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Burkov, Andriy. "Adaptive Dynamics Learning and Q-initialization in the Context of Multiagent Learning." Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24476/24476.pdf.

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L’apprentissage multiagent est une direction prometteuse de la recherche récente et à venir dans le contexte des systèmes intelligents. Si le cas mono-agent a été beaucoup étudié pendant les deux dernières décennies, le cas multiagent a été peu étudié vu sa complexité. Lorsque plusieurs agents autonomes apprennent et agissent simultanément, l’environnement devient strictement imprévisible et toutes les suppositions qui sont faites dans le cas mono-agent, telles que la stationnarité et la propriété markovienne, s’avèrent souvent inapplicables dans le contexte multiagent. Dans ce travail de maît
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Cunningham, Bryan. "Non-Reciprocating Sharing Methods in Cooperative Q-Learning Environments." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/34610.

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Past research on multi-agent simulation with cooperative reinforcement learning (RL) for homogeneous agents focuses on developing sharing strategies that are adopted and used by all agents in the environment. These sharing strategies are considered to be reciprocating because all participating agents have a predefined agreement regarding what type of information is shared, when it is shared, and how the participating agent's policies are subsequently updated. The sharing strategies are specifically designed around manipulating this shared information to improve learning performance. This thesi
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Andersson, Gabriel, and Martti Yap. "Att spela 'Breakout' med hjälp av 'Deep Q-Learning'." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255799.

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I denna rapport implementerar vi en reinforcement learning (RL) algoritm som lär sig spela Breakout på 'Atari Learning Environment'. Den dator drivna spelaren (Agenten) har tillgång till samma information som en mänsklig spelare och vet inget om spelet och dess regler på förhand. Målet är att reproducera tidigare resultat genom att optimera agenten så att den överträffar den typiska mänskliga medelpoängen. För att genomföra detta formaliserar vi problemet som en 'Markov decision Process'. VI applicerar 'Deep Q-learning' algoritmen med 'action masking' för att uppnå en optimal strategi. Vi finn
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Books on the topic "Q-learning"

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Wiederhold, Chuck. The Q-matrix: Cooperative learning and critical thinking. Kagan Cooperative Learning, 1995.

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Lu, Xiaoqi. Regularized Greedy Gradient Q-Learning with Mobile Health Applications. [publisher not identified], 2021.

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author, Pereira Penny, and Health Foundation (Great Britain), eds. Building Q: Learning from designing a large scale improvement community. The Health Foundation, 2016.

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National Advisory Council for Education and Training Targets. New national Learning targets for England for 2002: Q&A document on national, regional and local implementation. Department for Education and Employment, 2002.

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

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Kimple, James A. Eye Q and the efficient learner. Optometric Extension Program Foundation, Inc., 1997.

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Kim, Su-dong. In'gan kwa kyoyuk iyagi: Q lidŏsip. Yangsŏwŏn, 2011.

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Lacey, Greg. Q&A modern world history. Letts Educational, 1999.

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ʻAzzāwī, ʻAbd al-Raḥmān Ḥusayn. al- Manhajīyah al-tārīkhīyah fī al-ʻIrāq ilá q. 4 H. /10 M. Dār al-Shuʾūn al-Thaqāfīyah al-ʻĀmmah "Āfāq ʻArabīyah", 1988.

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Morrison, Liz. Project Manager: Q Learning (Q Learning S.). Hodder & Stoughton, 2003.

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Book chapters on the topic "Q-learning"

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Stone, Peter, Xin Jin, Jiawei Han, Sanjay Jain, and Frank Stephan. "Q-Learning." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_683.

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Stone, Peter. "Q-Learning." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_689-1.

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Stone, Peter. "Q-Learning." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_689.

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Li, Jinna, Frank L. Lewis, and Jialu Fan. "Interleaved Q-Learning." In Reinforcement Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9_6.

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Sengupta, Nandita, and Jaya Sil. "Q-Learning Classifier." In Intrusion Detection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2716-6_4.

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Hu, Zhihui, Yubin Jiang, Xinghong Ling, and Quan Liu. "Accurate Q-Learning." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_49.

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Liu, Mark. "Deep Q-Learning." In Machine Learning, Animated. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23383-17.

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Goldberg, Yair, Rui Song, and Michael R. Kosorok. "Adaptive Q-learning." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2013. http://dx.doi.org/10.1214/12-imscoll911.

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Stanko, Silvestr, and Karel Macek. "CVaR Q-Learning." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70594-7_14.

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Sanghi, Nimish. "Deep Q-Learning." In Deep Reinforcement Learning with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6809-4_6.

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Conference papers on the topic "Q-learning"

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Maity, Sreejeet, and Aritra Mitra. "Robust Q-Learning under Corrupted Rewards." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10885945.

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Chen, Caroline. "Bandwidth Estimation with Conservative Q-Learning." In 2024 Asian Conference on Communication and Networks (ASIANComNet). IEEE, 2024. https://doi.org/10.1109/asiancomnet63184.2024.10811075.

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Duran, Kubra, Mehmet Ozdem, Kerem Gursu, and Berk Canberk. "Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks." In 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE, 2024. https://doi.org/10.1109/wf-iot62078.2024.10811013.

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Shao, Zhengdao, Liansheng Zhuang, Jie Yan, and Liting Chen. "Conservative In-Distribution Q-Learning for Offline Reinforcement Learning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650768.

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Pichittanarak, Ratchapol, Kasit Pacharatam, Xavier Jonathon Blake, and Narong Aphiratsakun. "Q-Learning and Double Q-Learning with Sliding Mode Control to stabilize a Ball-and-Beam System." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902835.

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Kantasewi, Nitchakun, Sanparith Marukatat, Somying Thainimit, and Okumura Manabu. "Multi Q-Table Q-Learning." In 2019 10th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES). IEEE, 2019. http://dx.doi.org/10.1109/ictemsys.2019.8695963.

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Lu, Fan, Prashant G. Mehta, Sean P. Meyn, and Gergely Neu. "Convex Q-Learning." In 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483244.

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Reid, Cameron, and Snehasis Mukhopadhyay. "Mutual Q-learning." In 2020 3rd International Conference on Control and Robots (ICCR). IEEE, 2020. http://dx.doi.org/10.1109/iccr51572.2020.9344374.

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Tan, Tao, Hong Xie, and Defu Lian. "Adaptive Order Q-learning." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/547.

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This paper revisits the estimation bias control problem of Q-learning, motivated by the fact that the estimation bias is not always evil, i.e., some environments benefit from overestimation bias or underestimation bias, while others suffer from these biases. Different from previous coarse-grained bias control methods, this paper proposes a fine-grained bias control algorithm called Order Q-learning. It uses the order statistic of multiple independent Q-tables to control bias and flexibly meet the personalized bias needs of different environments, i.e., the bias can vary from underestimation bi
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Zhang, Zongzhang, Zhiyuan Pan, and Mykel J. Kochenderfer. "Weighted Double Q-learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/483.

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Q-learning is a popular reinforcement learning algorithm, but it can perform poorly in stochastic environments due to overestimating action values. Overestimation is due to the use of a single estimator that uses the maximum action value as an approximation for the maximum expected action value. To avoid overestimation in Q-learning, the double Q-learning algorithm was recently proposed, which uses the double estimator method. It uses two estimators from independent sets of experiences, with one estimator determining the maximizing action and the other providing the estimate of its value. Doub
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Reports on the topic "Q-learning"

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Martinson, Eric, Alexander Stoytchev, and Ronald Arkin. Robot Behavioral Selection Using Q-learning. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada640010.

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Goodrich, Michael A., and Morgan Quigley. Satisficing Q-Learning: Efficient Learning in Problems With Dichotomous Attributes. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada451568.

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Ceren, Roi, Prashant Doshi, Matthew Meisel, Adam Goodie, and Dan Hall. Behaviorally Modeling Games of Strategy Using Descriptive Q-learning. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada575140.

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Zhou, Yifu. Self-configured Elastic Database with Deep Q-Learning Approach. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1271.

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Tanai, Yertai, and Kamil Ciftci. A Comprehensive Study of Impacts of “Q” Bus Rapid Transit System on Blackstone Avenue. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2450.

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This study examines the dual impact of the Fresno Area Express (FAX) Q Line, a 15.7-mile Bus Rapid Transit (BRT) system launched in 2018, on the Fresno housing market and passenger satisfaction. The Q Line, designed to modernize public transit,features eco-friendly compressed natural gas (CNG) vehicles, real-time passenger information, and improved service efficiency.Housing market analysis focused on residential properties sold between 2012 and 2024, utilizing Geographic Information System(GIS) mapping to segment properties into three regions: the Q Line corridor, an outer buffer zone, and th
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Oakley, Louise. K4D International Nature Learning Journey Summary. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.129.

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The International Nature Learning Journey was developed to support FCDO and other government departments’ understanding, capacity and influence related to nature, particularly in the run-up to COP-26. A series of on-line seminars took place between May and August 2021 which involved an expert speaker on each topic, followed by a case study to provide practical illustrations, and a facilitated Q&amp;A with participants. Each session was chaired by an expert facilitator. Participants included advisors from across several government departments, including FCDO, Defra, BEIS and Treasury, with appr
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Liu, Tairan. Addressing Urban Traffic Congestion: A Deep Reinforcement Learning-Based Approach. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2322.

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In an innovative venture, the research team embarked on a mission to redefine urban traffic flow by introducing an automated way to manage traffic light timings. This project integrates two critical technologies, Deep Q-Networks (DQN) and Auto-encoders, into reinforcement learning, with the goal of making traffic smoother and reducing the all-too-common road congestion in simulated city environments. Deep Q-Networks (DQN) are a form of reinforcement learning algorithms that learns the best actions to take in various situations through trial and error. Auto-encoders, on the other hand, are tool
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Aydin, Orhun. Deep Q-Learning Framework for Quantitative Climate Change Adaptation Policy for Florida Road Network due to Extreme Precipitation. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317673.

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García Ferro, Luz Ángela, Elba N. Luna, Lorena Rodríguez, Micha Van Waesberghe, and Darinka Vásquez Jordán. Peer Assist. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0009034.

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This document describes the Peer Assist, which is a facilitated workshop, held face-to-face or virtually, in which a diverse group of participants from inside and/or outside the Bank share their experiences and insights with a team that wants to benefit from what others have learned, before it decides on a specific plan or course of action to deal with a significant upcoming challenge. Different from more informal peer to peer learning opportunities (e.g. networking, mentoring, Q&amp;A, BBLs), the Peer Assist is a structured process designed to tackle challenges of greater complexity, uncertai
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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancement
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