The DOI matches its Crossref record.
Reinforcement Learning: An Introduction
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Actor-Critic methods are a widely used class of reinforcement learning (RL) algorithms that integrate both policy-based and value-based approaches [80,81].”
From Adaptive Information Modulation: Designing Governance Mechanisms for Multi-Agent Artificial Intelligence Systems (2026) · cites this paper as [80]
In the citing paper
“Such attributions of causality are a precondition for learning,2”
From Two prefrontal-subcortical circuits estimate controllability and reflect its impact on learning (2026) · cites this paper as [3]
In the citing paper
“Another distinguishing aspect is that, in this framework, actions are triggered by goals through prediction errors, whereas in most reinforcement learning schemes, responses are triggered by stimuli via associative links – and prediction errors have only a role in learning (e.g., using Temporal Difference learning) (Sutton and Barto, 1998).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Sutton and Barto, 1998
In the citing paper
“and interpreted in the framework of reinforcement learning (RL) theory4-5.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [5]
Citations
Cited by
Sorted by citations
Items 201–300 of 1,274
Vamvoudakis, K. G., Lewis, F. L., & Hudas, G. R. (2012) · Automatica · doi:10.1016/j.automatica.2012.05.074 · Cited by 573
Exploration versus exploitation in space, mind, and society
Hills, T. T., Todd, P. M., Lazer, D. M. J., Redish, A. D., & Couzin, I. D. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.10.004 · Cited by 600
Coincident but Distinct Messages of Midbrain Dopamine and Striatal Tonically Active Neurons
Morris, G., Arkadir, D., Nevet, A., Vaadia, E., & Bergman, H. (2004) · Neuron · doi:10.1016/j.neuron.2004.06.012 · Cited by 574
Modares, H., Lewis, F. L., & Sistani, M. B. N. (2013) · Automatica · doi:10.1016/j.automatica.2013.09.043 · Cited by 574
How to train your robot with deep reinforcement learning: Lessons we have learned
Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570
Modares, H., Lewis, F. L., & Jiang, Z. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2441749 · Cited by 570
Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
Liang, L., Ye, H., & Li, G. Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933962 · Cited by 559
Natural actor–critic algorithms
Bhatnagar, S., Sutton, R. S., Ghavamzadeh, M., & Lee, M. (2009) · Automatica · doi:10.1016/j.automatica.2009.07.008 · Cited by 581
Self-citationLearning-Based Computation Offloading for IoT Devices With Energy Harvesting
Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890685 · Cited by 560
Distributional Reinforcement Learning With Quantile Regression
Dabney, W., Rowland, M., Bellemare, M., & Munos, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11791 · Cited by 612
Nieuwenhuis, S., Holroyd, C. B., Mol, N., & Coles, M. G. H. (2004) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2004.05.003 · Cited by 557
Neural Basis of Reinforcement Learning and Decision Making
Lee, D., Seo, H., & Jung, M. W. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150512 · Cited by 581
Baliki, M. N., Geha, P., Fields, H. L., & Apkarian, A. V. (2010) · Neuron · doi:10.1016/j.neuron.2010.03.002 · Cited by 572
Reinforcement Learning for True Adaptive Traffic Signal Control
Abdulhai, B., Pringle, R., & Karakoulas, G. J. (2003) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2003)129:3(278 · Cited by 608
Chen, R., Yang, B. Y., Shi, L., & Wang, S. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106778 · Cited by 555
Machine Learning for industrial applications: A comprehensive literature review
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114820 · Cited by 553
Hierarchical models of behavior and prefrontal function
Botvinick, M. M. (2008) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2008.02.009 · Cited by 532
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Wan, Z., Li, H., He, H., & Prokhorov, D. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2879572 · Cited by 548
Walsh, M. M., & Anderson, J. R. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.05.008 · Cited by 577
Proceedings of the Genetic and Evolutionary Computation Conference
(2022) · doi:10.1145/3512290 · Cited by 549
Vul (2014) · doi:10.1111/cogs.12101 · Cited by 502
Instance-based learning in dynamic decision making
González, C., Lerch, J. F., & Lebiere, C. J. (2003) · Cognitive Science · doi:10.1207/s15516709cog2704_2 · Cited by 581
Maia, T. V., & McClelland, J. L. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406666101 · Cited by 568
Learning to trade via direct reinforcement
Moody, J. E., & Saffell, M. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.935097 · Cited by 586
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535
Addiction as a Computational Process Gone Awry
Redish, A. D. (2004) · Science · doi:10.1126/science.1102384 · Cited by 556
Murphy, S., Frank, L., Chatterjee, J. S., & Baezconde-Garbanati, L. (2013) · Journal of Communications · doi:10.1111/jcom.12007 · Cited by 559
Practical Block-Wise Neural Network Architecture Generation
Zhong, Z., Yan, J., Wu, W., Shao, J., & Liu, C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00257 · Cited by 581
Human-like autonomous car-following model with deep reinforcement learning
Zhu, M., Wang, X., & Wang, Y. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.10.024 · Cited by 537
What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G
Geraci, G., García‐Rodríguez, A., Azari, M. M., Lozano, A., Mezzavilla, M., Chatzinotas, S., Chen, Y., Rangan, S., & Renzo, M. D. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3171135 · Cited by 541
Exploration in deep reinforcement learning: A survey
Ladosz, P., Weng, L., Kim, M., & Oh, H. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.003 · Cited by 525
“The most common technique for exploration in reinforcement learning is random exploration [1].”
MethodThe Role of Variability in Motor Learning
Dhawale, A. K., Smith, M. A., & Ölveczky, B. P. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031548 · Cited by 545
Uncertainty in perception and the Hierarchical Gaussian Filter
Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465
Infinite-Horizon Policy-Gradient Estimation
Baxter, J., & Bartlett, P. L. (2001) · Cited by 525
Vamvoudakis, K. G., & Lewis, F. L. (2011) · Automatica · doi:10.1016/j.automatica.2011.03.005 · Cited by 525
A stochastic model of human-machine interaction for learning dialog strategies
Levin, E., Pieraccini, R., & Eckert, W. (2000) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.817450 · Cited by 516
Chen, H., Jiang, B., Ding, S. X., & Huang, B. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3029946 · Cited by 528
Behavior Trees in Robotics and AI
Colledanchise, M., & Ögren, P. (2018) · CRC Press eBooks · doi:10.1201/9780429489105 · Cited by 534
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva
Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., et al. (2000) · The International Journal of Robotics Research · doi:10.1177/02783640022067922 · Cited by 523
ERP Correlates of Feedback and Reward Processing in the Presence and Absence of Response Choice
Yeung, N., Holroyd, C. B., & Cohen, J. D. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhh153 · Cited by 520
Perseus: Randomized point-based value iteration for POMDPs
Spaan, M. T. J., & Vlassis, N. A. (2005) · UvA-DARE (University of Amsterdam) · Cited by 526
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Burke, E. K., Kendall, G., & Soubeiga, E. (2003) · Journal of Heuristics · doi:10.1023/b:heur.0000012446.94732.b6 · Cited by 509
Midbrain dopamine neurons encode decisions for future action
Morris, G., Nevet, A., Arkadir, D., Vaadia, E., & Bergman, H. (2006) · Nature Neuroscience · doi:10.1038/nn1743 · Cited by 503
A unified framework for addiction: Vulnerabilities in the decision process
Redish, A. D., Jensen, S., & Johnson, A. (2008) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0800472x · Cited by 501
Reinforcement Learning based Recommender Systems: A Survey
Afsar, M. M., Crump, T., & Far, B. H. (2022) · ACM Computing Surveys · doi:10.1145/3543846 · Cited by 637
Reinforcement learning and episodic memory in humans and animals: an integrative framework
Gershman, S. J., & Daw, N. D. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033625 · Cited by 446
What is Machine Learning? A Primer for the Epidemiologist
Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz189 · Cited by 579
Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language
Hao, J., & Ho, T. K. (2019) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998619832248 · Cited by 557
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496
Posterior cingulate cortex: adapting behavior to a changing world
Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.02.002 · Cited by 504
Towards a neuroscience of active sampling and curiosity
Gottlieb, J., & Oudeyer, P.-Y. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0078-0 · Cited by 494
Chapter 16 Agent-Based Computational Economics: A Constructive Approach to Economic Theory
Tesfatsion, L. S. (2006) · Handbook of computational economics · doi:10.1016/s1574-0021(05)02016-2 · Cited by 494
PAC-Bayesian Generalization Error Bounds for Gaussian Process Classification
Seeger, M. (2003) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.1162/153244303765208377 · Cited by 515
DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS
Lau, B., & Glimcher, P. W. (2005) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2005.110-04 · Cited by 485
Active Object Localization with Deep Reinforcement Learning
Caicedo, J. C., & Lazebnik, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.286 · Cited by 459
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 568
Neural Circuitry of Reward Prediction Error
Watabe-Uchida, M., Eshel, N., & Uchida, N. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031109 · Cited by 437
Frank, M. J., Doll, B. B., Oas-Terpstra, J., & Moreno, F. (2009) · Nature Neuroscience · doi:10.1038/nn.2342 · Cited by 479
Dopamine: generalization and bonuses
Kakade, S. M., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00048-5 · Cited by 495
Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-3-12 · Cited by 480
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2727878 · Cited by 494
BlockDrop: Dynamic Inference Paths in Residual Networks
Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., & Feris, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00919 · Cited by 465
Frontal theta links prediction errors to behavioral adaptation in reinforcement learning
Cavanagh, J. F., Frank, M. J., Klein, T. J., & Allen, J. J. B. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.080 · Cited by 502
A Review of Generalized Zero-Shot Learning Methods
Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C. P., Wang, X., & Wu, Q. M. J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3191696 · Cited by 521
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Verma, S., Novati, G., & Koumoutsakos, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800923115 · Cited by 10
The computational neurobiology of learning and reward
Daw, N. D., & Doya, K. (2006) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2006.03.006 · Cited by 492
Reinforcement learning for control: Performance, stability, and deep approximators
Buşoniu, L., de Bruin, T., Tolić, D., Kober, J., & Palunko, I. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.005 · Cited by 490
The 2020 motile active matter roadmap
Gompper, G., Winkler, R. G., Speck, T., Solon, A. P., Nardini, C., Peruani, F., Löwen, H., Golestanian, R., Kaupp, U. B., Alvarez, L., et al. (2020) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ab6348 · Cited by 483
Machine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 499
Zhao, N., Liang, Y., Niyato, D. T., Pei, Y., Wu, M., & Jiang, Y. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2933417 · Cited by 475
Mechanisms underlying cortical activity during value-guided choice
Hunt, L. T., Kolling, N., Soltani, A., Woolrich, M. W., Rushworth, M. F. S., & Behrens, T. E. J. (2012) · Nature Neuroscience · doi:10.1038/nn.3017 · Cited by 454
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems
Wei, Q., Liu, D., & Lin, H. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2492242 · Cited by 468
Abdelrahman, G. M., Wang, Q., & Nunes, B. P. (2022) · ACM Computing Surveys · doi:10.1145/3569576 · Cited by 560
Zhu, M., Wang, Y., Pu, Z., Hu, J., Wang, X., & Ke, R. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102662 · Cited by 471
Planning-based prediction for pedestrians
Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K., & Srinivasa, S. (2009) · 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2009.5354147 · Cited by 456
Modares, H., Lewis, F. L., & Sistani, M. B. N. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2276571 · Cited by 466
Combining online and offline knowledge in UCT
Gelly, S., & Silver, D. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273531 · Cited by 491
Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
Tang, Y.-S., Tian, Y., Lu, J.-W., Li, P.-Y., & Zhou, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00558 · Cited by 383
Halilaj, E., Rajagopal, A., Fiterau, M., Hicks, J. L., Hastie, T. J., & Delp, S. L. (2018) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2018.09.009 · Cited by 511
Active learning of inverse models with intrinsically motivated goal exploration in robots
Baranès, A., & Oudeyer, P. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.05.008 · Cited by 498
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Li, L., Chu, W., Langford, J. C., & Wang, X. (2011) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1935826.1935878 · Cited by 447
Song replay during sleep and computational rules for sensorimotor vocal learning
Dave, A. S., & Margoliash, D. (2000) · Science · doi:10.1126/science.290.5492.812 · Cited by 458
Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach
Challita, U., Saad, W., & Bettstetter, C. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2900035 · Cited by 458
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2007) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2007.2055 · Cited by 476
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
Sezer, Ö. B., Doğdu, E., & Özbayoğlu, A. M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2773600 · Cited by 468
Performance variability enables adaptive plasticity of ‘crystallized’ adult birdsong
Tumer, E. C., & Brainard, M. S. (2007) · Nature · doi:10.1038/nature06390 · Cited by 453
Quantum-Enhanced Machine Learning
Dunjko, V., Taylor, J. M., & Briegel, H. J. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.130501 · Cited by 460
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
Cairano, S. D., Bernardini, D., Bemporad, A., & Kolmanovsky, I. V. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2272179 · Cited by 447
SoK: Towards the Science of security and privacy in machine learning
Papernot, N., McDaniel, P. D., Sinha, A., & Wellman, M. P. (2018) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 427
Wang, C., Wang, J., Shen, Y., & Zhang, X. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2890773 · Cited by 451
Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103535 · Cited by 445
Self-citationA survey on engineering approaches for self-adaptive systems
Krupitzer, C., Roth, F. M., VanSyckel, S., Schiele, G., & Becker, C. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.09.009 · Cited by 442
A computational and neural model of momentary subjective well-being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407535111 · Cited by 444
Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value
Tai, L.-H., Lee, A. M., Benavidez, N., Bonci, A., & Wilbrecht, L. (2012) · Nature Neuroscience · doi:10.1038/nn.3188 · Cited by 399
Keeping the Resident in the Loop: Adapting the Smart Home to the User
Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2009.2025137 · Cited by 443
An analysis of model-based Interval Estimation for Markov Decision Processes
Strehl, A. L., & Littman, M. L. (2008) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2007.08.009 · Cited by 428
Medial prefrontal cell activity signaling prediction errors of action values
Matsumoto, M., Matsumoto, K., Abe, H., & Tanaka, K. (2007) · Nature Neuroscience · doi:10.1038/nn1890 · Cited by 462
Neural mechanisms and temporal dynamics of performance monitoring
Ullsperger, M., Fischer, A. G., Nigbur, R., & Endraß, T. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.02.009 · Cited by 456
Huys, Q. J. M., Eshel, N., O'Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002410 · Cited by 439
Metadata
- Authors
- Sutton, R. S., & Barto, A. G.
- Affiliations
- MIT Press
- ORCID
- Sutton, R.S.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 1998-09
- Volume
- 9
- Issue
- 5
- Pages
- 1054-1054
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W4214717370
- Cited by
- 27,225
- References
- 0
- Citations by year
- 2026: 818 · 2025: 2403 · 2024: 2277 · 2023: 2382 · 2022: 2316 · 2021: 2022 · 2020: 1875 · 2019: 1731 · 2018: 1444 · 2017: 1058 · 2016: 852 · 2015: 782 · 2014: 892 · 2013: 817 · 2012: 816
- FWCI
- 95.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Networks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- reinforcement learning, learning from interaction, optimal control, Markov decision process, policy optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
