Reinforcement Learning: A Survey

Kaelbling, L. P., Littman, M. L., Moore, A. W.·1996·Journal of Artificial Intelligence Research

Cited by (1,024)

Items 501–600 of 1,024

  1. Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity
    Taehyeun Park, Nof Abuzainab, Walid Saad · 2016 · IEEE Access
  2. Potential to curb the environmental burdens of American beef consumption using a novel plant-based beef substitute
    Benjamin P. Goldstein, Rebekah Moses, Norman E. Sammons, Morten Birkved · 2017 · PLoS ONE
  3. Lung Nodule Detection via Deep Reinforcement Learning
    Issa M. Ali, Gregory R. Hart, Gowthaman Gunabushanam, Ying Liang, Wazir Muhammad, Bradley J. Nartowt, Michael J. Kane, Xiaomei Ma, Jun Deng · 2018 · Frontiers in Oncology
  4. A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning
    Arghya Bhowmik, Ivano E. Castelli, Juan María García-Lastra, Peter Bjørn Jørgensen, Ole Winther, Tejs Vegge · 2019 · Energy storage materials
  5. Learning from ambiguity
    Oded Maron, Tomás Lozano‐Pérez · 1998 · DSpace@MIT (Massachusetts Institute of Technology)
  6. Responsive characters from motion fragments
    James McCann, Nancy S. Pollard · 2007 · ACM SIGGRAPH Conference (SIGGRAPH)
  7. Sequencing delivery and receiving operations for yard cranes in port container terminals
    Kap Hwan Kim, Keung Mo Lee, Hark Hwang · 2003 · International Journal of Production Economics
  8. Data-Driven $H_\infty$ Control for Nonlinear Distributed Parameter Systems
    Biao Luo, Tingwen Huang, Huai‐Ning Wu, Xiong Yang · 2015 · IEEE Transactions on Neural Networks and Learning Systems
  9. On the Potential of Handover Parameter Optimization for Self-Organizing Networks
    Pablo Muñoz, Raquel Barco, Isabel de-la-Bandera · 2013 · IEEE Transactions on Vehicular Technology
  10. Comparing exploration strategies for Q-learning in random stochastic mazes
    Arryon D. Tijsma, Mădălina M. Drugan, Marco Wiering · 2016 · IEEE Symposium Series on Computational Intelligence (SSCI)
  11. MOVI: A Model-Free Approach to Dynamic Fleet Management
    Takuma Oda, Carlee Joe‐Wong · 2018 · IEEE Conference on Computer Communications (INFOCOM)
  12. Generative Network Complex for the Automated Generation of Drug-like Molecules
    Kaifu Gao, Duc Duy Nguyen, Meihua Tu, Guo‐Wei Wei · 2020 · Journal of Chemical Information and Modeling
  13. An On-Line Method to Evolve Behavior and to Control a Miniature Robot in Real Time with Genetic Programming
    Peter C. Nordin, Wolfgang Banzhaf · 1997 · Adaptive Behavior
  14. General mechanisms for making decisions?
    Matthew F. S. Rushworth, Rogier B. Mars, Christopher Summerfield · 2009 · Current Opinion in Neurobiology
  15. New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization
    M. Samrout, Farouk Yalaoui, Ėric Châtelet, Nazir Chebbo · 2004 · Reliability Engineering & System Safety
  16. Sequential Optimal Design of Neurophysiology Experiments
    Jeremy Lewi, Robert J. Butera, Liam Paninski · 2008 · Neural Computation
  17. Neural reinforcement learning for behaviour synthesis
    Claude F. Touzet · 1997 · Robotics and Autonomous Systems
  18. Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey
    Emre Güreş, Ibraheem Abdullah Mohammed Shayea, Mustafa Ergen, Marwan Hadri Azmi, Ayman A. El‐Saleh · 2022 · IEEE Access
  19. A Review on Machine Learning Strategies for Real-World Engineering Applications
    Rutvij H. Jhaveri, A. R. Revathi, Kadiyala Ramana, Roshani Raut, Rajesh Kumar Dhanaraj · 2022 · Mobile Information Systems
  20. Reinforcement learning for a biped robot based on a CPG-actor-critic method
    Yutaka Nakamura, Takeshi Mori, Masa-aki Sato, Shin Ishii · 2007 · Neural Networks
  21. Deep active inference as variational policy gradients
    Millidge, Beren · 2020 · Journal of Mathematical Psychology
  22. PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
    Jürgen Schmidhuber · 2013 · Frontiers in Psychology
  23. Tooth brushing as routine behaviour
    Robert V. Aunger · 2007 · International Dental Journal
  24. Efficient Processing of Deep Neural Networks
    Vivienne Sze, Yu‐Hsin Chen, Tien-Ju Yang, Joel Emer · 2020 · Synthesis lectures on computer architecture
  25. Reinforcement Learning: An Introduction, by Sutton, R.S. and Barto, A.G.
    P. Read Montague · 1999 · Trends in Cognitive Sciences
  26. The many faces of precision (Replies to commentaries on “Whatever next? Neural prediction, situated agents, and the future of cognitive science”)
    Clark, Andy · 2013 · Frontiers in Psychology
  27. Stochastic optimization of multireservoir systems via reinforcement learning
    Jinhee Lee, John W. Labadie · 2007 · Water Resources Research
  28. Automating spoken dialogue management design using machine learning: An industry perspective
    Tim Paek, Roberto Pieraccini · 2008 · Speech Communication
  29. Coordination of Multiple Robotic Fish With Applications to Underwater Robot Competition
    Junzhi Yu, Chen Wang, Guangming Xie · 2015 · IEEE Transactions on Industrial Electronics
  30. Enhancing zakat compliance through good governance: a conceptual framework
    Abdulsalam Ahmed Sawmar, Mustafa Omar Mohammed · 2021 · ISRA International Journal of Islamic Finance
  31. Neural Architecture Search Based on a Multi-Objective Evolutionary Algorithm With Probability Stack
    Yu Xue, Chen Chen, Adam Słowik · 2023 · IEEE Transactions on Evolutionary Computation
  32. Near Optimal Behavior via Approximate State Abstraction
    David Lynn Abel, D Ellis Hershkowitz, Michael L. Littman · 2017 · arXiv (Cornell University)
  33. Processes of adaptation in farm decision-making models. A review
    Marion Robert, Alban Thomas, Jacques-Éric Bergez · 2016 · Agronomy for Sustainable Development
  34. Event-Triggered Adaptive Dynamic Programming for Non-Zero-Sum Games of Unknown Nonlinear Systems via Generalized Fuzzy Hyperbolic Models
    Huaguang Zhang, Hanguang Su, Kun Zhang, Yanhong Luo · 2019 · IEEE Transactions on Fuzzy Systems
  35. Deep ${Q}$ -Learning-Based Node Positioning for Throughput-Optimal Communications in Dynamic UAV Swarm Network
    A. M. Koushik, Fei Hu, Sunil Kumar · 2019 · IEEE Transactions on Cognitive Communications and Networking
  36. Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks
    Wanlu Lei, Yu Ye, Ming Xiao · 2020 · IEEE Transactions on Cognitive Communications and Networking
  37. Learning motor skills: from algorithms to robot experiments
    Jens Kober · 2014 · it - Information Technology
  38. INFORM
    Raffaele Chiocchetti, Diego Perino, Giovanna Carofiglio, Dario Rossi, Giuseppe Rossini · 2013 · ACM Conference on Information-Centric Networking (ICN)
  39. Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning
    Paulo Valente Klaine, João Pedro Battistella Nadas, Richard Demo Souza, Muhammad Ali Imran · 2018 · Cognitive Computation
  40. AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions
    Huixian Gu, Liqiang Zhao, Zhu Han, Gan Zheng, Shenghui Song · 2023 · IEEE Communications Surveys & Tutorials
  41. Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions
    Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu · 2025 · Drones
  42. Minimax differential dynamic programming: application to a biped walking robot
    Jun Morimoto, Garth J. Zeglin, Christopher G. Atkeson · 2003 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  43. Team-partitioned, opaque-transition reinforcement learning
    Stone, Peter, Veloso, Manuela · 1999 · Proceedings of the third annual conference on Autonomous Agents
  44. Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
    Tiancheng Zhao, Kaige Xie, Maxine Eskénazi · 2019 · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
  45. AI-Based Resource Provisioning of IoE Services in 6G: A Deep Reinforcement Learning Approach
    Hani Sami, Hadi Otrok, Jamal Bentahar, Azzam Mourad · 2021 · IEEE Transactions on Network and Service Management
  46. Reinforcement learning-based multi-strategy cuckoo search algorithm for 3D UAV path planning
    Xiaobing Yu, Wenguan Luo · 2023 · Expert Systems with Applications
  47. Modeling embodied visual behaviors
    Nathan R. Sprague, Dana H. Ballard, A. E. Robinson · 2007 · ACM Transactions on Applied Perception
  48. A review of machine learning for automated planning
    Sergio Jiménez, Tomás de la Rosa, Susana Fernández, Fernando Fernández, Daniel Borrajo · 2012 · The Knowledge Engineering Review
  49. A survey of inverse reinforcement learning techniques
    Zhifei Shao, Er Meng Joo · 2012 · International Journal of Intelligent Computing and Cybernetics
  50. A real-world rational agent: unifying old and new AI
    Paul F. M. J. Verschure, Philipp Althaus · 2003 · Cognitive Science
  51. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
    Conrad D. James, James B. Aimone, Nadine E. Miner, Craig Michael Vineyard, Fredrick H. Rothganger, Kristofor D. Carlson, Samuel A. Mulder, Timothy J. Draelos, Aleksandra Faust, Matthew J. Marinella et al. · 2017 · Biologically Inspired Cognitive Architectures
  52. Reinforcement Learning-Based Routing Protocols for Vehicular Ad Hoc Networks: A Comparative Survey
    Rezoan Ahmed Nazib, Sangman Moh · 2021 · IEEE Access
  53. NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
    Yifan Wang, Linlin Zhong · 2023 · Journal of Computational Physics
  54. Federated Learning and Meta Learning: Approaches, Applications, and Directions
    Xiaonan Liu, Yansha Deng, Arumugam Nallanathan, Mehdi Bennis · 2023 · IEEE Communications Surveys & Tutorials
  55. A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities
    Arezou Naghib, Farhad Soleimanian Gharehchopogh, Azadeh Zamanifar · 2025 · Artificial Intelligence Review
  56. Artificial intelligence
    Gheorghe D. Tecuci · 2011 · Wiley Interdisciplinary Reviews Computational Statistics
  57. A reinforcement learning-based metaheuristic algorithm for solving global optimization problems
    Amir Seyyedabbasi · 2023 · Advances in Engineering Software
  58. Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization
    Bram Bakker, Jürgen Schmidhuber · 2004
  59. Evolutionary mechanism design: a review
    Steve Phelps, Peter McBurney, Simon Parsons · 2009 · Autonomous Agents and Multi-Agent Systems
  60. Gödel Machines: Fully Self-referential Optimal Universal Self-improvers
    Schmidhuber, Jürgen · 2007 · Cognitive Technologies
  61. Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey
    Sérgio Branco, André G. Ferreira, Jorge Cabral · 2019 · Electronics
  62. An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem
    Bingjie Li, Guohua Wu, Yong‐Ming He, Mingfeng Fan, Witold Pedrycz · 2022 · IEEE/CAA Journal of Automatica Sinica
  63. Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature
    Fahd, Kiran, Venkatraman, Sitalakshmi, Miah, Shah J., Ahmed, Khandakar · 2022 · Education and Information Technologies
  64. SemiACO: A semi-supervised feature selection based on ant colony optimization
    Fereshteh Karimi, Mohammad Bagher Dowlatshahi, Amin Hashemi · 2022 · Expert Systems with Applications
  65. A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks
    Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Xiangji Huang · 2024 · Computers in Biology and Medicine
  66. Changes in Performance Monitoring During Sensorimotor Adaptation
    Joaquin A. Anguera, Rachael D. Seidler, William J. Gehring · 2009 · Journal of Neurophysiology
  67. Modular Q-learning based multi-agent cooperation for robot soccer
    Kui-Hong Park, Yong‐Jae Kim, Jong-Hwan Kim · 2001 · Robotics and Autonomous Systems
  68. Q-learning approach to coordinated optimization of passenger inflow control with train skip-stopping on a urban rail transit line
    Zhibin Jiang, Jinjing Gu, Wei David Fan, Wei Liu, Bingqin Zhu · 2018 · Computers & Industrial Engineering
  69. Reinforcement Learning for Dynamic Microfluidic Control
    Oliver J. Dressler, Philip D. Howes, Jaebum Choo, Andrew J. deMello · 2018 · ACS Omega
  70. Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review
    Wei Zhang, Andrea Valencia, Ni‐Bin Chang · 2021 · IEEE Transactions on Neural Networks and Learning Systems
  71. Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval
    Katja Hofmann, Shimon Whiteson, Maarten de Rijke · 2012 · Information Retrieval
  72. Value‐based deep reinforcement learning for adaptive isolated intersection signal control
    Chia‐Hao Wan, Ming‐Chorng Hwang · 2018 · IET Intelligent Transport Systems
  73. Future cities and autonomous vehicles: analysis of the barriers to full adoption
    Nacer Eddine Bezai, Benachir Medjdoub, Amin Al‐Habaibeh, Moulay Larbi Chalal, Fodil Fadli · 2020 · Energy and Built Environment
  74. Neurostrategy
    Thomas C. Powell · 2011 · Strategic Management Journal
  75. Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda
    Marko Kureljusic, Erik Karger · 2023 · Journal of Applied Accounting Research
  76. Learning dynamic algorithm portfolios
    Matteo Gagliolo, Jürgen Schmidhuber · 2007 · Annals of Mathematics and Artificial Intelligence
  77. Planning with Markov Decision Processes
    Mausam Mausam, Andrey Kolobov · 2012 · Synthesis lectures on artificial intelligence and machine learning
  78. Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle
    Teng Liu, Yuan Zou, Dexing Liu, Fengchun Sun · 2015 · Energies
  79. Termination Analysis of Probabilistic Programs Through Positivstellensatz’s
    Krishnendu Chatterjee, Hongfei Fu, Amir Kafshdar Goharshady · 2016 · Lecture notes in computer science
  80. Data fusion based coverage optimization in heterogeneous sensor networks: A survey
    Xianjun Deng, Yalan Jiang, Laurence Tianruo Yang, Man Lin, Lingzhi Yi, Minghua Wang · 2018 · Information Fusion
  81. Modified deep learning and reinforcement learning for an incentive-based demand response model
    Lulu Wen, Kaile Zhou, Jun Li, Shanyong Wang · 2020 · Energy
  82. Artificial intelligence: revolutionizing cardiology with large language models
    Machteld Boonstra, Davy Weissenbacher, Jason H. Moore, Graciela Gonzalez‐Hernandez, Folkert Wouter Asselbergs · 2023 · European Heart Journal
  83. Simbad: An Autonomous Robot Simulation Package for Education and Research
    Louis Hugues, Nicolas Bredèche · 2006 · Lecture notes in computer science
  84. Reinforcement Learning with Human Teachers: Understanding How People Want to Teach Robots
    Andrea L. Thomaz, Guy Hoffman, Cynthia Breazeal · 2006 · IEEE RO-MAN
  85. Design and evaluation of learning algorithms for dynamic resource management in virtual networks
    Mijumbi, Rashid, Gorricho, Juan-Luis, Serrat, Joan, Claeys, Maxim, De Turck, Filip, Latre, Steven · 2014 · 2014 IEEE Network Operations and Management Symposium (NOMS)
  86. "Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator
    Xiangyu Zhao, Long Xia, Jiliang Tang, Dawei Yin · 2019 · ACM SIGWEB Newsletter
  87. Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task
    Ruud Wetzels, Joachim Vandekerckhove, Francis Tuerlinckx, Eric‐Jan Wagenmakers · 2009 · Journal of Mathematical Psychology
  88. Reinforcement learning for adaptive routing
    Leonid Peshkin, Virginia Savova · 2003 · International Joint Conference on Neural Networks (IJCNN)
  89. Eye Movements for Reward Maximization
    Nathan R. Sprague, Dana H. Ballard · 2003
  90. Agent-centered search
    Sven Koenig · 2001
  91. Programmable Reinforcement Learning Agents
    David André, Stuart Russell · 2000
  92. Interactive Character Animation Using Simulated Physics: A State‐of‐the‐Art Review
    Thomas Geijtenbeek, Nicolas Pronost · 2012 · Computer Graphics Forum
  93. A Reinforcement Learning-Based Sleep Scheduling Algorithm for Desired Area Coverage in Solar-Powered Wireless Sensor Networks
    Hongbin Chen, Xueyan Li, Feng Zhao · 2016 · IEEE Sensors Journal
  94. Sensitivity of Catchment Transit Times to Rainfall Variability Under Present and Future Climates
    Daniel C. Wilusz, Ciaran Joseph Harman, William P. Ball · 2017 · Water Resources Research
  95. Exploration Disrupts Choice-Predictive Signals and Alters Dynamics in Prefrontal Cortex
    R. Becket Ebitz, Eddy Albarran, Tirin Moore · 2017 · Neuron
  96. Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-Based Vehicular Ad Hoc Networks
    Md. Asif Hossain, Rafidah Md Noor, Kok‐Lim Alvin Yau, Saaidal Razalli Azzuhri, Muhammad Reza Z’aba, Ismail Ahmedy · 2020 · IEEE Access
  97. Design of a 5G Network Slice Extension With MEC UAVs Managed With Reinforcement Learning
    Giuseppe Faraci, Christian Grasso, Giovanni Schembra · 2020 · IEEE Journal on Selected Areas in Communications
  98. Domain Adaptive Fake News Detection via Reinforcement Learning
    Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, M. Mancenido, Huan Liu · 2022 · The Web Conference
  99. Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects
    Jessada Sresakoolchai, Sakdirat Kaewunruen · 2023 · Scientific Reports
  100. Methods for enabling real-time analysis in digital twins: A literature review
    Mohammad Sadegh Eshaghi, Cosmin Anitescu, Timon Rabczuk · 2024 · Computers & Structures