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Journal articles on the topic 'Q-learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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9

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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10

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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11

HORIUCHI, Tadashi, Akinori FUJINO, Osamu KATAI, and Tetsuo SAWARAGI. "Q-PSP Learning: An Exploitation-Oriented Q-Learning Algorithm and Its Applications." Transactions of the Society of Instrument and Control Engineers 35, no. 5 (1999): 645–53. http://dx.doi.org/10.9746/sicetr1965.35.645.

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12

Ghazanfari, Behzad, and Nasser Mozayani. "Enhancing Nash Q-learning and Team Q-learning mechanisms by using bottlenecks." Journal of Intelligent & Fuzzy Systems 26, no. 6 (2014): 2771–83. http://dx.doi.org/10.3233/ifs-130945.

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13

Kim, Min-Soeng, Sun-Gi Hong, and Ju-Jang Lee. "Self-Learning Fuzzy Logic Controller using Q-Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 5 (2000): 349–54. http://dx.doi.org/10.20965/jaciii.2000.p0349.

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Fuzzy logic controllers consist of if-then fuzzy rules generally adopted from a priori expert knowledge. However, it is not always easy or cheap to obtain expert knowledge. Q-learning can be used to acquire knowledge from experiences even without the model of the environment. The conventional Q-learning algorithm cannot deal with continuous states and continuous actions. However, the fuzzy logic controller can inherently receive continuous input values and generate continuous output values. Thus, in this paper, the Q-learning algorithm is incorporated into the fuzzy logic controller to compens

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14

Yang, Min-Gyu, Kuk-Hyun Ahn, and Jae-Bok Song. "Tidy-up Task Planner based on Q-learning." Journal of Korea Robotics Society 16, no. 1 (2021): 56–63. http://dx.doi.org/10.7746/jkros.2021.16.1.056.

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15

Moodie, Erica E. M., Nema Dean, and Yue Ru Sun. "Q-Learning: Flexible Learning About Useful Utilities." Statistics in Biosciences 6, no. 2 (2013): 223–43. http://dx.doi.org/10.1007/s12561-013-9103-z.

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16

Hatcho, Yasuyo, Kiyohiko Hattori, and Keiki Takadama. "Time Horizon Generalization in Reinforcement Learning: Generalizing Multiple Q-Tables in Q-Learning Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 6 (2009): 667–74. http://dx.doi.org/10.20965/jaciii.2009.p0667.

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This paper focuses on generalization in reinforcement learning from the time horizon viewpoint, exploring the method that generalizes multiple Q-tables in the multiagent reinforcement learning domain. For this purpose, we propose time horizon generalization for reinforcement learning, which consists of (1) Q-table selection method and (2) Q-table merge timing method, enabling agents to (1) select which Q-tables can be generalized from among many Q-tables and (2) determine when the selected Q-tables should be generalized. Intensive simulation on the bargaining game as sequential interaction gam

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17

Clifton, Jesse, and Eric Laber. "Q-Learning: Theory and Applications." Annual Review of Statistics and Its Application 7, no. 1 (2020): 279–301. http://dx.doi.org/10.1146/annurev-statistics-031219-041220.

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Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such a

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18

He, Ningxia. "Image Sampling Using Q-Learning." International Journal of Computer Science and Engineering 8, no. 1 (2021): 5–12. http://dx.doi.org/10.14445/23488387/ijcse-v8i1p102.

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19

Ganapathi Subramanian, Sriram, Matthew E. Taylor, Kate Larson, and Mark Crowley. "Multi-Agent Advisor Q-Learning." Journal of Artificial Intelligence Research 74 (May 5, 2022): 1–74. http://dx.doi.org/10.1613/jair.1.13445.

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In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible. However, many real-world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. An interesting question that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this paper, we provide a principled fram

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20

Hu, Yuepeng, Lehan Yang, and Yizhu Lou. "Path Planning with Q-Learning." Journal of Physics: Conference Series 1948, no. 1 (2021): 012038. http://dx.doi.org/10.1088/1742-6596/1948/1/012038.

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21

Sarigül, Mehmet, and Mutlu Avci. "Q LEARNING REGRESSION NEURAL NETWORK." Neural Network World 28, no. 5 (2018): 415–31. http://dx.doi.org/10.14311/nnw.2018.28.023.

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22

Kamanchi, Chandramouli, Raghuram Bharadwaj Diddigi, and Shalabh Bhatnagar. "Successive Over-Relaxation ${Q}$ -Learning." IEEE Control Systems Letters 4, no. 1 (2020): 55–60. http://dx.doi.org/10.1109/lcsys.2019.2921158.

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23

Patnaik, Srikanta, and N. P. Mahalik. "Multiagent coordination utilising Q-learning." International Journal of Automation and Control 1, no. 4 (2007): 377. http://dx.doi.org/10.1504/ijaac.2007.015863.

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24

Lecué, Guillaume, and Philippe Rigollet. "Optimal learning with Q-aggregation." Annals of Statistics 42, no. 1 (2014): 211–24. http://dx.doi.org/10.1214/13-aos1190.

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25

Ahmadabadi, M. N., and M. Asadpour. "Expertness based cooperative Q-learning." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 1 (2002): 66–76. http://dx.doi.org/10.1109/3477.979961.

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26

Linn, Kristin A., Eric B. Laber, and Leonard A. Stefanski. "Interactive Q-Learning for Quantiles." Journal of the American Statistical Association 112, no. 518 (2017): 638–49. http://dx.doi.org/10.1080/01621459.2016.1155993.

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27

Goldberg, Yair, and Michael R. Kosorok. "Q-learning with censored data." Annals of Statistics 40, no. 1 (2012): 529–60. http://dx.doi.org/10.1214/12-aos968.

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28

Peng, Jing, and Ronald J. Williams. "Incremental multi-step Q-learning." Machine Learning 22, no. 1-3 (1996): 283–90. http://dx.doi.org/10.1007/bf00114731.

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29

El Wafi, Mouna, My Abdelkader Youssefi, Rachid Dakir, and Mohamed Bakir. "Intelligent Robot in Unknown Environments: Walk Path Using Q-Learning and Deep Q-Learning." Automation 6, no. 1 (2025): 12. https://doi.org/10.3390/automation6010012.

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Autonomous navigation is essential for mobile robots to efficiently operate in complex environments. This study investigates Q-learning and Deep Q-learning to improve navigation performance. The research examines their effectiveness in complex maze configurations, focusing on how the epsilon-greedy strategy influences the agent’s ability to reach its goal in minimal time using Q-learning. A distinctive aspect of this work is the adaptive tuning of hyperparameters, where alpha and gamma values are dynamically adjusted throughout training. This eliminates the need for manually fixed parameters a

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30

Mustafa, Hasan Kathim, Azma Zakaria Nurul, Abidin Z.Zainal, Kamil Maseer Ziadoon, and Hasan Alzamili Ali. "Online Sequential Extreme Learning Machine (OSELM) based Q-learning(OSELM-QL)." Seybold Report V16, no. 11 (2021): 1–14. https://doi.org/10.5281/zenodo.6553518.

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ABSTRACT The usage of reinforcement learning (RL) for many type of applications is increasing. The quick development of machine learning models in the recent years has motivated researchers to integrate Q-learning with deep learning which has opened the door for many vision based applications of RL. However, using RL with shallow types of neural network has not been tackled adequately in the literature despite its need for real time types of applications such as control systems or time constraint decision based system. In this article, we propose a novel online sequential extreme learning mach

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31

HOSOYA, Yu, and Motohide UMANO. "Improvement of Updating Method of Q Values in Fuzzy Q-Learning." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 6 (2015): 942–48. http://dx.doi.org/10.3156/jsoft.27.942.

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32

Duryea, Ethan, Michael Ganger, and Wei Hu. "Exploring Deep Reinforcement Learning with Multi Q-Learning." Intelligent Control and Automation 07, no. 04 (2016): 129–44. http://dx.doi.org/10.4236/ica.2016.74012.

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33

Hwang, Kao-Shing, Wei-Cheng Jiang, and Yu-Jen Chen. "ADAPTIVE MODEL LEARNING BASED ON DYNA-Q LEARNING." Cybernetics and Systems 44, no. 8 (2013): 641–62. http://dx.doi.org/10.1080/01969722.2013.803387.

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34

BOCHOK, Viacheslav, and Nataliia FEDOROVA. "CENTRALIZED LEARNING FOR THE DEEP Q-LEARNING MODELS." Information Technology and Society, no. 2 (13) (2024): 6–11. http://dx.doi.org/10.32689/maup.it.2024.2.1.

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35

da Costa, Luis Antonio L. F., Rafael Kunst, and Edison Pignaton de Freitas. "Q-FANET: Improved Q-learning based routing protocol for FANETs." Computer Networks 198 (October 2021): 108379. http://dx.doi.org/10.1016/j.comnet.2021.108379.

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36

Meng, Xiao-Li. "Discussion: The Q-q Dynamic for Deeper Learning and Research." International Statistical Review 84, no. 2 (2015): 181–89. http://dx.doi.org/10.1111/insr.12151.

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37

Guo, Yanqin. "Enhancing Flappy Bird Performance With Q-Learning and DQN Strategies." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 396–402. http://dx.doi.org/10.54097/qrded191.

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Flappy Bird, a classic single-player game, boasts a deceptively simple premise yet proves to be a formidable challenge in achieving high scores. Various algorithms have been employed to improve its performance, yet a comprehensive assessment of Q-Learning and Deep Q-Network (DQN) in the context of this game remains elusive. This study undertakes the task of training Flappy Bird using both Q-Learning and DQN methodologies, showcasing the potency of reinforcement learning within the realm of gaming. Through meticulous comparisons and analyses, the paper uncovers the inherent strengths and weakne

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38

D'Orazio, Tiziana, and Grazia Cicirelli. "Q-Learning: computation of optimal Q-values for evaluating the learning level in robotic tasks." Journal of Experimental & Theoretical Artificial Intelligence 13, no. 3 (2001): 241–70. http://dx.doi.org/10.1080/09528130110063100.

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39

Chen, Bo-Wei, Shih-Hung Yang, Yu-Chun Lo, et al. "Enhancement of Hippocampal Spatial Decoding Using a Dynamic Q-Learning Method With a Relative Reward Using Theta Phase Precession." International Journal of Neural Systems 30, no. 09 (2020): 2050048. http://dx.doi.org/10.1142/s0129065720500483.

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Hippocampal place cells and interneurons in mammals have stable place fields and theta phase precession profiles that encode spatial environmental information. Hippocampal CA1 neurons can represent the animal’s location and prospective information about the goal location. Reinforcement learning (RL) algorithms such as Q-learning have been used to build the navigation models. However, the traditional Q-learning ([Formula: see text]Q-learning) limits the reward function once the animals arrive at the goal location, leading to unsatisfactory location accuracy and convergence rates. Therefore, we

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40

Liu, Peiyi. "Q-Learning: Applications and Convergence Rate Optimization." Highlights in Science, Engineering and Technology 63 (August 8, 2023): 210–15. http://dx.doi.org/10.54097/hset.v63i.10878.

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As an important algorithm of artificial intelligence technology, Q-learning algorithm plays a significant part in a number of fields, such as driverless technology, industrial automation, health care, intelligent search, game, etc. As a classical learning algorithm in reinforcement learning, with the help of an experienced action sequence in a Markov environment, an agent can learn to select the best course of action using the model-free learning technique known as Q-learning. This paper mainly discusses the addition of received signal strength (RSS) to the Q-learning algorithm to navigate unm

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41

Raza, Ali, Asfand Ali, Alaptageen Qayyum, Ghulam Shabir, Zahid Hussain, and Ghulam Murtaza. "HYPERPARAMETER IMPACT ON LEARNING EFFICIENCY IN Q-LEARNING AND DQN USING OPENAI GYMNASIUM ENVIRONMENTS." International Journal of Advanced Research 13, no. 05 (2025): 1164–76. https://doi.org/10.21474/ijar01/21007.

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This study compares the Q-learning and DQN methodologies within the CartPole-v1 environment. All methods were executed, trained, and evaluated according to test outcomes and training improvements. The research paper includes statistical summaries, and visualizations of performance and learning. This paper examines the impact of hyperparameters on the learning efficiency of Q-Learning and Deep Q-Network (DQN) in the CartPole-v1 environment of OpenAI Gymnasium. The results indicate that DQN substantially outperforms Q-Learning. DQN achieved a peak training reward of 500, whereas Q-Learning maint

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42

古, 彭. "Improvement and Implementation of Q-Learning Algorithm." Computer Science and Application 11, no. 07 (2021): 1994–2007. http://dx.doi.org/10.12677/csa.2021.117204.

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43

Xu, Shenghua, Yang Gu, Xiaoyan Li, et al. "Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm." ISPRS International Journal of Geo-Information 11, no. 1 (2022): 66. http://dx.doi.org/10.3390/ijgi11010066.

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The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path plan

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44

Zhang, Chunyuan, Qi Song, and Zeng Meng. "Minibatch Recursive Least Squares Q-Learning." Computational Intelligence and Neuroscience 2021 (October 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/5370281.

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The deep Q-network (DQN) is one of the most successful reinforcement learning algorithms, but it has some drawbacks such as slow convergence and instability. In contrast, the traditional reinforcement learning algorithms with linear function approximation usually have faster convergence and better stability, although they easily suffer from the curse of dimensionality. In recent years, many improvements to DQN have been made, but they seldom make use of the advantage of traditional algorithms to improve DQN. In this paper, we propose a novel Q-learning algorithm with linear function approximat

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45

Takashi, Sato and Fumiko Shirasaki NIT Okinawa College Japan. "A Comparative Study on the Performances of Q-Learning and Neural Q-Learning Agents toward Analysis of Emergence of Communication." Journal of Information and Communication Engineering(JICE) Volume 3, Issue 5 (2020): 128–35. https://doi.org/10.5281/zenodo.4309746.

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Abstract: In this paper, we suppose the gesture theory that is one theory on the origin of language, which tries to establish that speech originated from gestures. Based on the theory, we assume that “actions” having some purposes can be used as “symbols” in the communication through a learning process. The purpose of this study is to clarify what abilities of agents and what conditions are necessary to acquire usages of the actions as the symbols. To investigate them, we adopt a collision avoidance game and compare the performances of Q-learning agents with that of Neu

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46

Wei-Kai Sun, Wei-Kai Sun, Xiao-Mei Wang Wei-Kai Sun, Bin Wang Xiao-Mei Wang, Jia-Sen Zhang Bin Wang, and Hai-Yang Du Jia-Sen Zhang. "MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks." 電腦學刊 35, no. 1 (2024): 051–63. http://dx.doi.org/10.53106/199115992024023501004.

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<p>In recent years, with the rapid development of science and technology, many new technologies have made people&rsquo;s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol

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47

Md Nurul Raihen and Jason Tran. "Optimizing reinforcement learning in complex environments using neural networks." International Journal of Science and Research Archive 12, no. 2 (2024): 2047–62. http://dx.doi.org/10.30574/ijsra.2024.12.2.1471.

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This paper presents the distinct mechanisms and applications of traditional Q-learning (QL) and Deep Q-learning (DQL) within the realm of reinforcement learning (RL). Traditional Q-learning (QL) utilizes the Bellman equation to update Q-values stored in a Q-table, making it suitable for simple environments. However, its scalability is limited due to the exponential growth of state-action pairs in complex environments. Deep Q-learning (DQL) addresses this limitation by using neural networks to approximate Q-values, thus eliminating the need for a Q-table, and enabling efficient handling of comp

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48

Hao, Qixuan. "The Achievement of Dynamic Obstacle Avoidance Based on Improved Q-Learning Algorithm." Highlights in Science, Engineering and Technology 63 (August 8, 2023): 252–58. http://dx.doi.org/10.54097/hset.v63i.10883.

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Dynamic obstacle avoidance is a classic problem in robot control, which involves the ability of a robot to avoid obstacles in the environment and reach its destination. Among various path planning algorithms, the dynamic obstacle avoidance issue may be resolved using the reinforcement learning algorithm Q-learning. This article provides a comprehensive review of the recent research progress and achievements in the field of dynamic obstacle avoidance, through the analysis and improvement of the Q-learning algorithm. The article begins by introducing the background and research status of dynamic

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49

Shin, YongWoo. "Q-learning to improve learning speed using Minimax algorithm." Journal of Korea Game Society 18, no. 4 (2018): 99–106. http://dx.doi.org/10.7583/jkgs.2018.18.4.99.

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50

Xu, Haoran, Xianyuan Zhan, and Xiangyu Zhu. "Constraints Penalized Q-learning for Safe Offline Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8753–60. http://dx.doi.org/10.1609/aaai.v36i8.20855.

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We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We s

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