Reinforcement Learning: A Survey

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Items 801–900 of 1,024

1. RLMD‐PA: A Reinforcement Learning‐Based Myocarditis Diagnosis Combined with a Population‐Based Algorithm for Pretraining Weights
   Seyed Vahid Moravvej, Roohallah Alizadehsani, Sadia Khanam, Zahra Sobhaninia, Afshin Shoeibi, Fahime Khozeimeh, Zahra Alizadeh Sani, Ru‐San Tan, Abbas Khosravi, Saeid Nahavandi et al. · 2022 · Contrast Media & Molecular Imaging
2. Human-autonomy Teaming: Need for a guiding team-based framework?
   Tom O’Neill, Christopher Flathmann, Nathan J. McNeese, Eduardo Salas · 2023 · Computers in Human Behavior
3. Pruning an ensemble of classifiers via reinforcement learning
   Ioannis Partalas, Grigorios Tsoumakas, Ioannis P. Vlahavas · 2008 · Neurocomputing
4. Hybrid Metaheuristics
   El‐Ghazali Talbi · 2012 · Studies in computational intelligence
5. Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits
   Paul B. Reverdy, Vaibhav Srivastava, Naomi Ehrich Leonard · 2014 · Proceedings of the IEEE
6. SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems
   Ankush Desai, Shromona Ghosh, Sanjit Arunkumar Seshia, Natarajan Shankar, Ashish Tiwari · 2019 · International Conference on Dependable Systems & Networks/Proceedings
7. Quantum Machine Learning: A Review and Current Status
   Nimish Mishra, Manik Kapil, H R Rakesh, Amit Kumar Anand, Nilima Mishra, Aakash Warke, Soumya Sarkar, Sanchayan Dutta, Sabhyata Gupta, Aditya Prasad Dash et al. · 2020 · Advances in intelligent systems and computing
8. A Multi-Agent Collaborative Environment Learning Method for UAV Deployment and Resource Allocation
   Zhaojun Dai, Yan Zhang, Wancheng Zhang, Xinran Luo, Zunwen He · 2022 · IEEE Transactions on Signal and Information Processing over Networks
9. A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
   Guangxi Yan, Chen Jiang, Yu Dong Bai, Chengqing Yu, Chengming Yu · 2022 · Processes
10. A survey on automatic generation of medical imaging reports based on deep learning
    Ting Pang, Peigao Li, Lijie Zhao · 2023 · BioMedical Engineering OnLine
11. Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review
    Miguel Martínez-Comesaña, Xurxo Rigueira, Ana Larrañaga, Javier Martínez Martínez, Iago Ocarranza-Prado, Denis Kreibel · 2023 · Revista de Psicodidáctica (English ed )
12. A Review on Wireless Networked Control System: The Communication Perspective
    Ying Wang, Shaohua Wu, Chengjia Lei, Jian Jiao, Qinyu Zhang · 2023 · IEEE Internet of Things Journal
13. Dynamic Game Balancing: an Evaluation of User Satisfaction
    Gustavo Danzi de Andrade, Geber Lisboa Ramalho, Alex Sandro Gomes, Vincent Corruble · 2006 · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
14. National-scale analysis of simulated hydrological droughts (1891–2015)
    Alison C. Rudd, Victoria A. Bell, Alison Lindsey Kay · 2017 · Journal of Hydrology
15. Internal Models and Anticipations in Adaptive Learning Systems
    Martin V. Butz, Olivier Sigaud, Pierre Gérard · 2003 · Lecture notes in computer science
16. Young people’s travel behavior – Using the life-oriented approach to understand the acceptance of autonomous driving
    Bernd Herrenkind, Ilja Nastjuk, Benedikt Brendel, Simon Thanh-Nam Trang, Lutz Maria Kolbe · 2019 · Transportation Research Part D Transport and Environment
17. Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks
    Yuan Zhou, Tao Cao, Wei Xiang · 2020 · IEEE Internet of Things Journal
18. The Human Affectome
    Daniela Schiller, Alessandra Nicoletta Cruz Yu, Nelly Alia‐Klein, Susanne Becker, Howard C. Cromwell, Florin Dolcos, Paul J. Eslinger, Paul Frewen, Andrew Haddon Kemp, Edward Franz Pace-Schott et al. · 2023 · Neuroscience & Biobehavioral Reviews
19. Optimizing debt collections using constrained reinforcement learning
    Naoki Abe, Prem Melville, Cezar Pendus, Chandan K. Reddy, David L. Jensen, Vince P. Thomas, James J. Bennett, Gary F. Anderson, Brent R. Cooley, Melissa Kowalczyk et al. · 2010 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
20. Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner
    Kurt Driessens, Jan Ramon, Hendrik Blockeel · 2001 · Lecture notes in computer science
21. A bias–variance trade-off governs individual differences in on-line learning in an unpredictable environment
    Christopher M. Glaze, Alexandre L. S. Filipowicz, Joseph W. Kable, Vijay Balasubramanian, Joshua I. Gold · 2018 · Nature Human Behaviour
22. Increasing Motor Noise Impairs Reinforcement Learning in Healthy Individuals
    Amanda S. Therrien, Daniel M. Wolpert, Amy J. Bastian · 2018 · eNeuro
23. Recent Advances in Neural Question Generation
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24. Dynamic Replication and Hedging: A Reinforcement Learning Approach
    Petter N. Kolm, Gordon Ritter · 2019 · The Journal of Financial Data Science
25. Deep learning radiomics in breast cancer with different modalities: Overview and future
    Ting Pang, Jeannie Hsiu Ding Wong, Wei Lin Ng, Chee Seng Chan · 2020 · Expert Systems with Applications
26. Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters
    Luca Caviglione, Mauro Gaggero, Massimo Paolucci, Roberto Ronco · 2020 · Soft Computing
27. Impacto de la inteligencia artificial en los métodos de evaluación en la educación primaria y secundaria: revisión sistemática de la literatura
    Miguel Martínez-Comesaña, Xurxo Rigueira, Ana Larrañaga, Javier Martínez Martínez, Iago Ocarranza-Prado, Denis Kreibel · 2023 · Revista de Psicodidáctica
28. Anticipatory Behavior: Exploiting Knowledge About the Future to Improve Current Behavior
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29. Distributed Learning Agents in Urban Traffic Control
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30. Penalized Q-learning for dynamic treatment regimens
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31. Learning competitive pricing strategies by multi-agent reinforcement learning
    E. Kutschinski, Thomas Uthmann, Daniel Polani · 2003 · Journal of Economic Dynamics and Control
32. A reinforcement learning based routing protocol with QoS support for biomedical sensor networks
    Xuedong Liang, Ilangko Balasingham, Sang‐Seon Byun · 2008 · International Symposium on Applied Sciences in Biomedical and Communication Technologies
33. Selective Processes in Development: Implications for the Costs and Benefits of Phenotypic Plasticity
    Emilie C. Snell‐Rood · 2012 · Integrative and Comparative Biology
34. State-Dependent Exploration for Policy Gradient Methods
    Thomas Rückstieß, Martin Felder, Jürgen Schmidhuber · 2008 · Lecture notes in computer science
35. Accelerating autonomous learning by using heuristic selection of actions
    Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa · 2007 · Journal of Heuristics
36. Single view reconstruction using shape grammars for urban environments
    Panagiotis Koutsourakis, Loïc Simon, Olivier Teboul, Georgios Tziritas, Nikos Paragios A · 2009 · IEEE/CVF International Conference on Computer Vision (ICCV)
37. Controlled gliding and perching through deep-reinforcement-learning
    Guido Novati, L. Mahadevan, Petros Koumoutsakos · 2019 · Physical Review Fluids
38. Dopamine, Learning, and Impulsivity: ABiological Account of Attention-Deficit/Hyperactivity Disorder
    Jonathan H. Williams, Peter Dayan · 2005 · Journal of Child and Adolescent Psychopharmacology
39. Methamphetamine-Induced Disruption of Frontostriatal Reward Learning Signals: Relation to Psychotic Symptoms
    Bernacer, Javier, Corlett, Philip R., Ramachandra, Pranathi, McFarlane, Brady, Turner, Danielle C., Clark, Luke, Robbins, Trevor W., Fletcher, Paul C., Murray, Graham K. · 2013 · American Journal of Psychiatry
40. An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution
    Sami Kabir, Raihan Ul Islam, Mohammad Shahadat Hossain, Karl Andersson · 2020 · Sensors
41. Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach
    Haoran Wang, Thaleia Zariphopoulou, Xun Yu Zhou · 2020 · Journal of Machine Learning Research
42. Deep reinforcement learning with the confusion-matrix-based dynamic reward function for customer credit scoring
    Yadong Wang, Yanlin Jia, Yuhang Tian, Jin Xiao · 2022 · Expert Systems with Applications
43. A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects
    Dhiren Rohera, Harshal Shethna, Keyur Patel, Urvish Thakker, Sudeep Tanwar, Rajesh Gupta, Wei‐Chiang Hong, Ravi Sharma · 2022 · IEEE Access
44. Label-efficient learning in agriculture: A comprehensive review
    Jiajia Li, Dong Chen, Xinda Qi, Zhaojian Li, Yanbo Huang, Daniel Morris, Xiaobo Tan · 2023 · Computers and Electronics in Agriculture
45. Deep Learning in Multimodal Fusion for Sustainable Plant Care: A Comprehensive Review
    Zhixiang Yang, Yusi Li, Rui-Feng Wang, Pingfan Hu, Wen‐Hao Su · 2025 · Sustainability
46. The Cambridge Handbook of Psychology and Economic Behaviour
    2008 · Cambridge University Press eBooks
47. A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution
    Dieter Hendricks, Diane Wilcox · 2014 · IEEE Conference on Computational Intelligence for Financial Engineering & Economics
48. Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client
    Maxim Claeys, Steven Latré, Jeroen Famaey, Tingyao Wu, Werner Van Leekwijck, Filip De Turck · 2014 · Connection Science
49. A Survey of Intelligence Methods in Urban Traffic Signal Control
    Liu Zhi-yong · 2007
50. An Introduction to Learning Fuzzy Classifier Systems
    Andrea Bonarini · 2000 · Lecture notes in computer science
51. Computational Models of Dialogue
    Jonathan Ginzburg, Raquel Fernández · 2010
52. Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning
    Zhang, Yutong, Di, Boya, Zheng, Zijie, Lin, Jinlong, Song, Lingyang · 2021 · IEEE Transactions on Wireless Communications
53. Conjectural Equilibrium in Multiagent Learning
    Wellman, Michael P., Hu, Junling · 1998 · Machine Learning
54. Efficient and scalable reinforcement learning for large-scale network control
    Chengdong Ma, Aming Li, Yali Du, Hao Dong, Yaodong Yang · 2024 · Nature Machine Intelligence
55. Sampling-based planning, control and verification of hybrid systems
    Michael S. Branicky, Michael M. Curtiss, Joshua A. Levine, Stuart Morgan · 2006 · IEE Proceedings - Control Theory and Applications
56. Probabilistic MDP-behavior planning for cars
    Sebastian Brechtel, Tobias Gindele, Rüdiger Dillmann · 2011 · IEEE International Conference on Intelligent Transportation Systems (ITSC)
57. Heuristically-Accelerated Multiagent Reinforcement Learning
    Reinaldo A. C. Bianchi, Murilo Fernandes Martins, Carlos H. C. Ribeiro, Anna Helena Reali Costa · 2013 · IEEE Transactions on Cybernetics
58. Adaptive treatment of epilepsy via batch-mode reinforcement learning
    Arthur Guez, Robert D. Vincent, Massimo Avoli, Joëlle Pineau · 2008
59. Policy-Gradient Algorithms for Partially Observable Markov Decision Processes
    Douglas Aberdeen · 2003 · ANU Open Research (Australian National University)
60. RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
    Chaudhari, Shreyas, Aggarwal, Pranjal, Murahari, Vishvak, Rajpurohit, Tanmay, Kalyan, Ashwin, Narasimhan, Karthik, Deshpande, Ameet, Castro da Silva, Bruno · 2026 · ACM Computing Surveys
61. Nonparametric Return Distribution Approximation for Reinforcement Learning
    Tetsuro Morimura, Masashi Sugiyama, M. Sugiyama, H. Kashima, Toshiyuki Tanaka, H. Hachiya, T. Tanaka · 2010
62. Dreaming neural networks: Forgetting spurious memories and reinforcing pure ones
    Alberto Fachechi, Elena Agliari, Adriano Barra A · 2019 · Neural Networks
63. Artificial intelligence in nuclear industry: Chimera or solution?
    Siddharth Suman · 2020 · Journal of Cleaner Production
64. Machine Learning Approaches in Battery Management Systems: State of the Art: Remaining useful life and fault detection
    Reza Rouhi Ardeshiri, Bharat Balagopal, Amro Alsabbagh, Chengbin Ma, Mo–Yuen Chow · 2020 · IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)
65. Multiparameter optimisation of a magneto-optical trap using deep learning
    Aaron D. Tranter, Harry J. Slatyer, Michael Hush, Anthony C. Leung, Jesse L. Everett, Karun Paul, Pierre Vernaz-Gris, Ping Koy Lam, B. C. Buchler, Geoff Campbell · 2018 · RePEc: Research Papers in Economics
66. Architectural spatial layout planning using artificial intelligence
    Jaechang Ko, Benjamin Ennemoser, Won-Jae Yoo, Wei Yan, Mark J. Clayton · 2023 · Automation in Construction
67. Application of Artificial Intelligence in Tissue Engineering
    Reza Bagherpour, Ghasem Bagherpour, Parvin Mohammadi · 2024 · Tissue Engineering Part B Reviews
68. A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions
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69. Challenge-Sensitive Action Selection: an Application to Game Balancing
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70. Theoretical frameworks for neuroeconomics of intertemporal choice.
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71. Relational Reinforcement Learning
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72. A Survey of Deep Learning Applications to Autonomous Vehicle Control
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73. Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
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74. When blockchain meets AI: Optimal mining strategy achieved by machine learning
    Taotao Wang, Soung Chang Liew, Shengli Zhang · 2021 · International Journal of Intelligent Systems
75. Information metasurfaces and intelligent metasurfaces
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76. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review
    Misaal Khan, Mahapara Khurshid, Mayank Vatsa, Richa Singh, Mona Duggal, Kuldeep Singh · 2022 · Frontiers in Public Health
77. Intelligent technologies for construction machinery using data-driven methods
    Zhe Zheng, Fei Wang, Guofang Gong, Huayong Yang, Dong-Seop Han · 2022 · Automation in Construction
78. Reinforcement Learning in Education: A Literature Review
    Bisni Fahad Mon, Asma Wasfi, Mohammad Hayajneh, Ahmad Slim, Najah A. Abu Ali · 2023 · Informatics
79. Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning
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80. Monte Carlo hyper-heuristics for examination timetabling
    Edmund K. Burke, Graham Kendall, Mustafa Mısır, Ender Özcan · 2010 · Annals of Operations Research
81. An Integrated Framework for Human–Robot Collaborative Manipulation
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82. Curiosity driven reinforcement learning for motion planning on humanoids
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83. Neural networks in wireless networks: Techniques, applications and guidelines
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84. Bayesian Probability Theory: Applications in the Physical Sciences
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85. An evolutionary approach for the tuning of a chess evaluation function using population dynamics
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86. A multifaceted vision of the human-AI collaboration: a comprehensive review
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87. HetMEC: Heterogeneous Multi-Layer Mobile Edge Computing in the 6 G Era
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88. A bottom up approach towards the acquisition and expression of sequential representations applied to a behaving real-world device: Distributed Adaptive Control III
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89. Fuzzy Rule-Based Reinforcement Learning for Load Balancing Techniques in Enterprise LTE Femtocells
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90. Dynamic Opinion Maximization in Social Networks
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91. SmartNoshWaste: Using Blockchain, Machine Learning, Cloud Computing and QR Code to Reduce Food Waste in Decentralized Web 3.0 Enabled Smart Cities
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92. A survey of machine learning techniques for improving Global Navigation Satellite Systems
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93. Reinforcement learning for electric vehicle charging scheduling: A systematic review
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94. Dopamine, prediction error and associative learning: A model-based account
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95. Derivation of Centers and Axes of Rotation for Wrist and Fingers in a Hand Kinematic Model: Methods and Reliability Results
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96. An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations
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97. Learning to Take Actions
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98. Time Limits in Reinforcement Learning
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99. A reinforcement learning approach for waterflooding optimization in petroleum reservoirs
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100. Should I take over? Does system knowledge help drivers in making take-over decisions while driving a partially automated car?
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