Reinforcement Learning: A Survey

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Items 701–800 of 1,024

1. TREATING EPILEPSY VIA ADAPTIVE NEUROSTIMULATION: A REINFORCEMENT LEARNING APPROACH
   Joëlle Pineau, Arthur Guez, ROBERT VINCENT, Gabriella Panuccio, Massimo Avoli · 2009 · International Journal of Neural Systems
2. Graph kernels and Gaussian processes for relational reinforcement learning
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3. IntelligenTester - Software Test Sequence Optimization Using Graph Based Intelligent Search Agent
   D. Jeya Mala · 2007 · International Conference on Computational Intelligence and Multimedia Applications (ICCIMA)
4. Making reinforcement learning work on real robots
   Leslie Pack Kaelbling, William D. Smart · 2002
5. Using reinforcement learning to optimize occupant comfort and energy usage in HVAC systems
   Pedro Fazenda, Kalyan K. Veeramachaneni, Pedro U. Lima, Una-May O’Reilly · 2014 · Journal of Ambient Intelligence and Smart Environments
6. Is memory a natural kind?
   Kourken Michaelian · 2010 · Memory Studies
7. Deep Reinforcement Learning as a Job Shop Scheduling Solver: A Literature Review
   Bruno Cunha, Ana Maria Madureira, Benjamim Fonseca, Duarte Coelho · 2019 · Advances in intelligent systems and computing
8. Generative Steganography via Auto-Generation of Semantic Object Contours
   Zhili Zhou, Xiaohua Dong, Ruohan Meng, Meimin Wang, Hongyang Yan, Keping Yu, Kim‐Kwang Raymond Choo · 2023 · IEEE Transactions on Information Forensics and Security
9. Systematic review on neural architecture search
   Sasan Salmani Pour Avval, Nathan Eskue, Roger M. Groves, Vahid Yaghoubi · 2025 · Artificial Intelligence Review
10. Effects of feedback delay on learning
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11. Integrating Guidance into Relational Reinforcement Learning
    Kurt Driessens, Sašo Džeroski · 2004 · Machine Learning
12. Verification for Machine Learning, Autonomy, and Neural Networks Survey
    Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel P. Hamilton, Xiaodong Yang, Joel A. Rosenfeld, Taylor Thomas Johnson · 2018 · arXiv (Cornell University)
13. Traffic Light Control by Multiagent Reinforcement Learning Systems
    Bram Bakker, Shimon Whiteson, Leon J. H. M. Kester, Frans C. A. Groen · 2010 · Studies in computational intelligence
14. A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks
    Wenbo Wang, Andres Kwasinski, Dusit Tao Niyato, Zhu Han · 2016 · IEEE Communications Surveys & Tutorials
15. NeuroVectorizer: end-to-end vectorization with deep reinforcement learning
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16. A Trust Update Mechanism Based on Reinforcement Learning in Underwater Acoustic Sensor Networks
    Yu Qing He, Guangjie Han, Jinfang Jiang, Hao Wang, Miguel Martínez-García · 2020 · IEEE Transactions on Mobile Computing
17. DRLR: A Deep-Reinforcement-Learning-Based Recruitment Scheme for Massive Data Collections in 6G-Based IoT Networks
    Ting Li, Wei Liu, Zhiwen Zeng, Naixue N. Xiong · 2021 · IEEE Internet of Things Journal
18. Applying Neural Network to Health Estimation and Lifetime Prediction of Lithium-Ion Batteries
    Penghua Li, Xiankui Wu, Radu Grosu, Jie Hou, Мамадшо Илолов, Sheng Xiang · 2024 · IEEE Transactions on Transportation Electrification
19. Learning in groups of traffic signals
    Ana L. C. Bazzan, Denise de Oliveira, Bruno C. da Silva · 2009 · Engineering Applications of Artificial Intelligence
20. Learning and exploration in action-perception loops
    Daniel Ying-Jeh Little, Friedrich T. Sommer · 2013 · Frontiers in Neural Circuits
21. Stochastic invariants for probabilistic termination
    Krishnendu Chatterjee, Petr Novotný, Đorđe Žikelić · 2016 · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL)
22. Shaping robot behavior using principles from instrumental conditioning
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23. Nomadic radio
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24. Social is special: A normative framework for teaching with and learning from evaluative feedback
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25. A review of machine learning methods used for educational data
    Ersozlu, Zara, Taheri, Sona, Koch, Inge · 2024 · Education and Information Technologies
26. A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots
    Wei Dong Zhu, Xian Guo, Dai Owaki, Kyo Kutsuzawa, Mitsuhiro Hayashibe · 2021 · IEEE Transactions on Neural Networks and Learning Systems
27. A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning
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28. A review of machine learning models applied to genomic prediction in animal breeding
    Narjice Chafai, Ichrak Hayah, Isidore Houaga, Bouabid Badaoui · 2023 · Frontiers in Genetics
29. Adaptive Service Composition Based on Reinforcement Learning
    Hongbing Wang, Xuan Zhou, Xiang Yang Zhou, Weihong Liu, Wenya Li, Athman Bouguettaya · 2010 · Lecture notes in computer science
30. The Past, Present, and Future of Artificial Life
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31. FALCON: a fusion architecture for learning, cognition, and navigation
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32. Machine learning strategies for path-planning microswimmers in turbulent flows
    Alageshan, Jaya Kumar, Verma, Akhilesh Kumar, Bec, Jérémie, Pandit, Rahul · 2020 · Physical Review E
33. Conservatism and the Scientific State of Nature
    Erich Kummerfeld, Kevin Zollman · 2015 · The British Journal for the Philosophy of Science
34. A Symbiotic Brain-Machine Interface through Value-Based Decision Making
    Babak Mahmoudi, Justin C. Sanchez · 2011 · PLoS ONE
35. Hyper-heuristic approach for multi-objective software module clustering
    A. Charan Kumari, K. Srinivas · 2016 · Journal of Systems and Software
36. Attacker-Centric View of a Detection Game against Advanced Persistent Threats
    Liang Xiao, Dongjin Xu, Narayan B. Mandayam, H. Vincent Poor · 2018 · IEEE Transactions on Mobile Computing
37. Is Attention Really Effort? Revisiting Daniel Kahneman’s Influential 1973 Book Attention and Effort
    Brian J. Bruya, Yi‐Yuan Tang · 2018 · Frontiers in Psychology
38. AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review
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39. Towards the automation of systematic reviews using natural language processing, machine learning, and deep learning: a comprehensive review
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40. A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem
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41. Win-win search
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42. Robot Learning Driven by Emotions
    Sandra Clara Gadanho, John C. T. Hallam · 2001 · Adaptive Behavior
43. Performance analysis of the AntNet algorithm
    S.S. Dhillon, Piet Van Mieghem · 2006 · Computer Networks
44. Relational Reinforcement Learning: An Overview
    Prasad V. Tadepalli, Robert L. Givan, Kurt Driessens · 2004 · Lirias
45. Complex and Adaptive Dynamical Systems: A Primer
    Claudius Gros · 2008
46. Bandits for Taxonomies: A Model-based Approach
    Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti, Vanja Josifovski · 2007 · SIAM International Conference on Data Mining
47. Utilizing query change for session search
    Dongyi Guan, Sicong Zhang, Grace Hui Yang · 2013 · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
48. Mobile Big Data: The Fuel for Data-Driven Wireless
    Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Robert Cui · 2017 · IEEE Internet of Things Journal
49. Hybrid metaheuristics and multi-agent systems for solving optimization problems: A review of frameworks and a comparative analysis
    Maria Amélia Lopes Silva, Sérgio Ricardo de Souza, Marcone Jamilson Freitas Souza, Moacir Felizardo de França Filho · 2018 · Applied Soft Computing
50. Advances and challenges in deep generative models for de novo molecule generation
    Dongyu Xue, Yukang Gong, Zhaoyi Yang, Guohui Chuai, Sheng Qu, Aizong Shen, Jing Yu, Qi Liu · 2018 · Wiley Interdisciplinary Reviews Computational Molecular Science
51. A survey of modeling for prognosis and health management of industrial equipment
    Yigit Yucesan, Arinan De Piemonte Dourado, Felipe Antonio Chegury Viana · 2021 · Advanced Engineering Informatics
52. In-Network Machine Learning Using Programmable Network Devices: A Survey
    Changgang Zheng, Xinpeng Hong, Damu Ding, Shay Vargaftik, Yaniv Ben-Itzhak, Noa Zilberman · 2023 · IEEE Communications Surveys & Tutorials
53. Discriminative, generative and imitative learning
    Tony Jebara, Alex Pentland · 2002
54. Psychological models of human and optimal performance in bandit problems
    Michael David Lee, Shunan Zhang, Miles Munro, Mark Steyvers · 2010 · Cognitive Systems Research
55. Probabilistic Policy Reuse for inter-task transfer learning
    Fernando Fernández, Javier García, Manuela Veloso · 2010 · Robotics and Autonomous Systems
56. Cross-Entropy Optimization of Control Policies With Adaptive Basis Functions
    Lucian Buşoniu, Damien Ernst, Bart De Schutter, Robert Babuška · 2010 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
57. A Roadmap Towards Machine Intelligence
    Tomáš Mikolov, Armand Joulin, Marco Baroni · 2018 · Lecture notes in computer science
58. Detection of Region Duplication Forgery in Digital Images Using Wavelets and Log-Polar Mapping
    A. N. Myna, M.G. Venkateshmurthy, C.G Patil · 2007 · International Conference on Computational Intelligence and Multimedia Applications (ICCIMA)
59. An object-oriented representation for efficient reinforcement learning
    Diuk, Carlos, Cohen, Andre, Littman, Michael L. · 2008 · Proceedings of the 25th international conference on Machine learning - ICML '08
60. Deep Reinforcement Learning for Anomaly Detection: A Systematic Review
    Kinza Arshad, Rao Faizan Ali, Amgad Muneer, Izzatdin Abdul Aziz, Sheraz Naseer, Nabeel Sabir Khan, Shakirah Mohd Taib · 2022 · IEEE Access
61. Human Computation
    Edith Law, Luis von Ahn · 2011 · Synthesis lectures on artificial intelligence and machine learning
62. Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
    Rob Ashmore, Radu Călinescu, Colin Paterson · 2019 · arXiv (Cornell University)
63. A Survey on Applications of Machine Learning for Optimal Power Flow
    Hasan, Fouad, Kargarian, Amin, Mohammadi, Ali · 2020 · 2020 IEEE Texas Power and Energy Conference (TPEC)
64. Safe Exploration of State and Action Spaces in Reinforcement Learning
    J. Garcia, F. Fernandez · 2012 · Journal of Artificial Intelligence Research
65. Investigating the applications of artificial intelligence in cyber security
    Naveed Naeem Abbas, Tanveer Ahmed, Syed Habib Ullah Shah, Muhammad Omar, Han Woo Park · 2019 · Scientometrics
66. Q-Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing
    Jianjiao Ji, Yinan Guo, Xiao‐Zhi Gao, Dunwei Gong, Yapeng Wang · 2021 · IEEE Transactions on Cybernetics
67. Development of a neural network-based energy management system for a plug-in hybrid electric vehicle
    Federico Millo, Luciano Rolando, Luigi Tresca, Luca Pulvirenti · 2022 · Transportation Engineering
68. Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty
    Nicolas Meuleau, Paul Bourgine · 1999 · Machine Learning
69. Complex and Adaptive Dynamical Systems
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70. Complex problem-solving: a field in search of a definition?
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71. Natural Language Generation as Planning under Uncertainty for Spoken Dialogue Systems
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72. WILDSENSING
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73. A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks
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74. Collective Intelligence, Data Routing and Braess' Paradox
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75. Approximate Dynamic Programming Strategies and Their Applicability for Process Control: A Review and Future Directions
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76. A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization
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77. Universal Algorithmic Intelligence: A Mathematical Top→Down Approach
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78. Comprehensive comparison of online ADP algorithms for continuous-time optimal control
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79. UAV-IoT for Next Generation Virtual Reality
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80. Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning
    Hemant Rathore, Sanjay K. Sahay, Piyush Nikam, Mohit Sewak · 2020 · Information Systems Frontiers
81. Security of Satellite-Terrestrial Communications: Challenges and Potential Solutions
    Ijaz Ahmad, Jani Suomalainen, Pawani Porambage, Andrei Gurtov, Jyrki Huusko, Marko Höyhtyä · 2022 · IEEE Access
82. Hyper-heuristics: A survey and taxonomy
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83. Multi-Objective Hyper-Heuristic Approaches for Space Allocation and Timetabling
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84. Efficient reinforcement learning: computational theories, neuroscience and robotics
    Mitsuo Kawato, Kazuyuki Samejima · 2007 · Current Opinion in Neurobiology
85. Safe Exploration Techniques for Reinforcement Learning – An Overview
    Martin Pecka, Tomáš Svoboda · 2014 · Lecture notes in computer science
86. Returning is Believing
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87. Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5
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88. A survey on hybrid human-artificial intelligence for autonomous driving
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89. Particle Swarm Optimization for Compact Neural Architecture Search for Image Classification
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90. Optimistic Planning of Deterministic Systems
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91. An Overview of Cooperative and Competitive Multiagent Learning
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92. Reinforcement learning and evolutionary algorithms for non-stationary multi-armed bandit problems
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93. Improving Analysis Pattern Reuse in Conceptual Design: Augmenting Automated Processes with Supervised Learning
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94. Probabilistic inference for solving (PO) MDPs
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95. Selective impairment of prediction error signaling in human dorsolateral but not ventral striatum in Parkinson's disease patients: evidence from a model-based fMRI study
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96. Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition
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97. Which and How Many Regions to Gaze: Focus Discriminative Regions for Fine-Grained Visual Categorization
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98. A Continuous-Decision Virtual Network Embedding Scheme Relying on Reinforcement Learning
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99. Safe Exploration of State and Action Spaces in Reinforcement Learning
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100. Reinforcement learning for robotic flow shop scheduling with processing time variations
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