Reinforcement Learning: An Introduction

AdaFrame: Adaptive Frame Selection for Fast Video Recognition

Wu, Z., Xiong, C., Ma, C., Socher, R., & Davis, L. S. (2019) · 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.2019.00137 · Cited by 225

Circuit Mechanisms of Sensorimotor Learning

Makino, H., Hwang, E. J., Hedrick, N. G., & Komiyama, T. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.029 · Cited by 228

Integral Reinforcement Learning for Linear Continuous-Time Zero-Sum Games With Completely Unknown Dynamics

Li, H., Liu, D., & Wang, D. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2300532 · Cited by 234

Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

Kim, B., & Pineau, J. (2015) · International Journal of Social Robotics · doi:10.1007/s12369-015-0310-2 · Cited by 228

Deep reinforcement learning for inventory control: A roadmap

Boute, R. N., Gijsbrechts, J., van Jaarsveld, W. L., & Vanvuchelen, N. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.07.016 · Cited by 275

Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path

Antos, A., Szepesvári, C., & Munos, R. (2007) · Machine Learning · doi:10.1007/s10994-007-5038-2 · Cited by 222

Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization

Lewis, R. L., Howes, A., & Singh, S. P. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12086 · Cited by 246

Migration Modeling and Learning Algorithms for Containers in Fog Computing

Tang, Z., Zhou, X., Zhang, F., Weijia Jia, B., & Zhao, W. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2827070 · Cited by 229

Learning Policies for Adaptive Tracking with Deep Feature Cascades

Huang, C., Lucey, S., & Ramanan, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.21 · Cited by 238

Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions

Krugel, L. K., Biele, G., Mohr, P. N. C., Li, S.-C., & Heekeren, H. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905191106 · Cited by 229

Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized

Dezfouli, A., & Balleine, B. W. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003364 · Cited by 239

A Survey on Resource Allocation in Vehicular Networks

Noor‐A‐Rahim, M., Liu, Z., Lee, H., Ali, G. G. M. N., Pesch, D., & Xiao, P. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3019322 · Cited by 245

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Li, N., Martí­nez-Ortega, J.-F., Chaus, J. M. M., & Eckert, M. (2016) · Sensors · doi:10.3390/s16030414 · Cited by 236

Neurocomputational mechanisms of prosocial learning and links to empathy

Lockwood, P. L., Apps, M. A. J., Valton, V., Viding, E. M., & Roiser, J. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1603198113 · Cited by 219

Machine Learning in the Air

Gündüz, D., de Kerret, P., Sidiropoulos, N. D., Gesbert, D., Murthy, C. R., & van der Schaar, M. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933969 · Cited by 222

A Concise Introduction to Models and Methods for Automated Planning

Geffner, H., & Bonet, B. (2013) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00513ed1v01y201306aim022 · Cited by 233

Automatic collision avoidance of multiple ships based on deep Q-learning

Shen, H., Hashimoto, H., Matsuda, A., Taniguchi, Y., Terada, D., & Guo, C. G. (2019) · Applied Ocean Research · doi:10.1016/j.apor.2019.02.020 · Cited by 236

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review

Buchlak, Q. D., Esmaili, N., Leveque, J., Farrokhi, F. R., Bennett, C. C., Piccardi, M., & Sethi, R. K. (2019) · Neurosurgical Review · doi:10.1007/s10143-019-01163-8 · Cited by 234

COLREGs-compliant multiship collision avoidance based on deep reinforcement learning

Zhao, L., & Roh, M.-I. (2019) · Ocean Engineering · doi:10.1016/j.oceaneng.2019.106436 · Cited by 229

Vision-language models for medical report generation and visual question answering: a review

Hartsock, I., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1430984 · Cited by 298

Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

Wang, D., Liu, D., & Wei, Q. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.03.058 · Cited by 225

Adaptive dynamic programming and optimal control of nonlinear nonaffine systems

Bian, T., Jiang, Y., & Jiang, Z. (2014) · Automatica · doi:10.1016/j.automatica.2014.08.023 · Cited by 223

Real-time scheduling for a smart factory using a reinforcement learning approach

Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225

Internal models in biological control

McNamee, D., & Wolpert, D. M. (2019) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105206 · Cited by 225

Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex

Boorman, E. D., Behrens, T. E. J., & Rushworth, M. F. S. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001093 · Cited by 237

A Survey of Multi-Agent Trust Management Systems

Yu, H., Shen, Z., Leung, C., Miao, C., & Lesser, V. (2013) · IEEE Access · doi:10.1109/access.2013.2259892 · Cited by 221

Deep Learning in Protein Structural Modeling and Design

Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227

Explainable Reinforcement Learning: A Survey and Comparative Review

Milani, S., Topin, N., Veloso, M., & Fang, F. (2023) · ACM Computing Surveys · doi:10.1145/3616864 · Cited by 347

Incremental learning of full body motion primitives and their sequencing through human motion observation

Kulić, D., Ott, C., Lee, D., et al. (2011) · The International Journal of Robotics Research · doi:10.1177/0278364911426178 · Cited by 224

Converging Structural and Functional Connectivity of Orbitofrontal, Dorsolateral Prefrontal, and Posterior Parietal Cortex in the Human Striatum

Jarbo, K., & Verstynen, T. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2636-14.2015 · Cited by 228

A Primer on Foraging and the Explore/Exploit Trade-Off for Psychiatry Research

Addicott, M. A., Pearson, J. M., Sweitzer, M. M., Barack, D. L., & Platt, M. L. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.108 · Cited by 219

Adversarial environment reinforcement learning algorithm for intrusion detection

Caminero, G., López‐Martín, M., & Carro, B. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.05.013 · Cited by 234

Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development

Hauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D., & Brem, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.018 · Cited by 203

Learning and Innovative Elements of Strategy Adoption Rules Expand Cooperative Network Topologies

Wang, S., Szalay, M., Zhang, C., & Csermely, P. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001917 · Cited by 227

Optimized Assistive Human–Robot Interaction Using Reinforcement Learning

Modares, H., Ranatunga, I., Lewis, F. L., & Popa, D. O. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2412554 · Cited by 218

Gaze following: why (not) learn it?

Triesch, J., Teuscher, C., Deák, G. O., & Carlson, E. (2006) · Developmental Science · doi:10.1111/j.1467-7687.2006.00470.x · Cited by 225

UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process

Ragi, S., & Chong, E. K. P. (2013) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2013.6621824 · Cited by 229

An application of deep reinforcement learning to algorithmic trading

Théate, T., & Ernst, D. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114632 · Cited by 31

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control

Luo, B., Liu, D., Wu, H., Wang, D., & Lewis, F. L. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2623859 · Cited by 224

Choice history biases subsequent evidence accumulation

Urai, A. E., de Gee, J. W., Tsetsos, K., & Donner, T. H. (2019) · eLife · doi:10.7554/elife.46331 · Cited by 237

A deep reinforcement learning-based multi-optimality routing scheme for dynamic IoT networks

Cong, P., Zhang, Y., Liu, Z., Baker, T., Tawfik, H., Wang, W., Xu, K., Li, R., & Li, F. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108057 · Cited by 225

A three-network architecture for on-line learning and optimization based on adaptive dynamic programming

He, H., Ni, Z., & Fu, J. Y. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.05.031 · Cited by 221

Interactive Policy Learning through Confidence-Based Autonomy

Chernova, S., & Veloso, M. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2584 · Cited by 218

Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Emary, E., Zawbaa, H. M., & Groşan, C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2634548 · Cited by 224

A Mechanism for Error Detection in Speeded Response Time Tasks.

Holroyd, C. B., Yeung, N., COLES, M. G. H., & Cohen, J. D. (2005) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.134.2.163 · Cited by 227

Reinforcement Learning Versus Model Predictive Control: A Comparison on a Power System Problem

Ernst, D., Glavić, M., Capitanescu, F., & Wehenkel, L. A. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2007630 · Cited by 214

Optimized Adaptive Nonlinear Tracking Control Using Actor–Critic Reinforcement Learning Strategy

Wen, G., Chen, C. L. P., Ge, S. S., Yang, H., & Liu, X. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2894282 · Cited by 221

Habitual Behavior Is Goal-Driven

Kruglanski, A. W., & Szumowska, E. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620917676 · Cited by 231

A Bayesian analysis of human decision-making on bandit problems

Steyvers, M., Lee, M. D., & Wagenmakers, E. (2009) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2008.11.002 · Cited by 209

Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

Chen, Z., & Wang, X. (2020) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-020-01801-6 · Cited by 227

First return, then explore

Ecoffet, A., Huizinga, J., Lehman, J., Stanley, K. O., & Clune, J. (2021) · Nature · doi:10.1038/s41586-020-03157-9 · Cited by 330

Cellular Network Traffic Scheduling With Deep Reinforcement Learning

Chinchali, S., Hu, P., Chu, T., Sharma, M., Bansal, M., Misra, R., Pavone, M., & Katti, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11339 · Cited by 218

Homeostatic reinforcement learning for integrating reward collection and physiological stability

Keramati, M., & Gutkin, B. (2014) · eLife · doi:10.7554/elife.04811 · Cited by 218

What do the basal ganglia do? A modeling perspective

Chakravarthy, V. S., Joseph, D., & Bapi, R. S. (2010) · Biological Cybernetics · doi:10.1007/s00422-010-0401-y · Cited by 214

A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms

Yu, J., & Buyya, R. (2006) · Workshop on Workflows in Support of Large-Scale Science (WORKS) · doi:10.1109/works.2006.5282330 · Cited by 218

Power Systems Stability Control: Reinforcement Learning Framework

Ernst, D., Glavić, M., & Wehenkel, L. A. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2003.821457 · Cited by 214

Adaptive integration of habits into depth-limited planning defines a habitual-goal–directed spectrum

Keramati, M., Smittenaar, P., Dolan, R. J., & Dayan, P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1609094113 · Cited by 205

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

Radmanesh, M., Kumar, M., Guentert, P. H., & Sarim, M. (2018) · Unmanned Systems · doi:10.1142/s2301385018400022 · Cited by 225

The macaque anterior cingulate cortex translates counterfactual choice value into actual behavioral change

Fouragnan, E. F., Chau, B. K. H., Folloni, D., Kolling, N., Verhagen, L., Klein-Flügge, M. C., Tankelevitch, L., Papageorgiou, G., Aubry, J., Sallet, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0375-6 · Cited by 207

Solving Statistical Mechanics Using Variational Autoregressive Networks

Wu, D., Wang, L., & Zhang, P. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.080602 · Cited by 250

Deep-Reinforcement-Learning-Based Autonomous UAV Navigation With Sparse Rewards

Wang, C., Wang, J., Wang, J., & Zhang, X. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2973193 · Cited by 219

Self-tuning experience weighted attraction learning in games

Ho, T., Camerer, C. F., & Chong, J.-K. (2006) · Journal of Economic Theory · doi:10.1016/j.jet.2005.12.008 · Cited by 202

Distinct Eligibility Traces for LTP and LTD in Cortical Synapses

He, K., Huertas, M. A., Hong, S. Z., Tie, X., Hell, J. W., Shouval, H. Z., & Kirkwood, A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.037 · Cited by 220

Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach

Zhang, K., He, F., Zhang, Z., et al. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102861 · Cited by 217

Planning and navigation as active inference

Kaplan, R., & Friston, K. J. (2018) · Biological Cybernetics · doi:10.1007/s00422-018-0753-2 · Cited by 203

Environmentally mediated synergy between perception and behaviour in mobile robots

Verschure, P. F. M. J., Voegtlin, T., & Douglas, R. J. (2003) · Nature · doi:10.1038/nature02024 · Cited by 219

Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality

Ma, N., Aviv, D., Guo, H., & Braham, W. W. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110436 · Cited by 235

Computational Models of Interoception and Body Regulation

Petzschner, F. H., Garfinkel, S. N., Paulus, M. P., Koch, C., & Khalsa, S. S. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.012 · Cited by 227

Artificial intelligence in drug discovery: applications and techniques

Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab430 · Cited by 244

Optimal control with learned local models: Application to dexterous manipulation

Kumar, V., Todorov, E. V., & Levine, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487156 · Cited by 207

Theory of mind as inverse reinforcement learning

Jara‐Ettinger, J. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.04.010 · Cited by 220

Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles

Hong, J., Wang, Z., Chen, W., Wang, L. Y., & Qu, C. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101459 · Cited by 225

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

Musaddiq, A., Nain, Z., Qadri, Y. A., Ali, R., & Kim, S. W. (2020) · Sensors · doi:10.3390/s20154158 · Cited by 217

Joint Optimization of Handover Control and Power Allocation Based on Multi-Agent Deep Reinforcement Learning

Guo, D., Tang, L., Zhang, X., & Liang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3020400 · Cited by 215

Routing optimization meets Machine Intelligence: A perspective for the future network

Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., & Xu, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.093 · Cited by 215

Artificial intelligence in environmental monitoring: in-depth analysis

Alotaibi, E., & Nassif, N. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00198-1 · Cited by 285

A video game description language for model-based or interactive learning

Schaul, T. (2013) · 2013 IEEE Conference on Computational Inteligence in Games (CIG) · doi:10.1109/cig.2013.6633610 · Cited by 214

Planning and Scheduling under Uncertainty: A Review Across Multiple Sectors

Verderame, P. M., Elia, J. A., Li, J., et al. (2010) · Industrial & Engineering Chemistry Research · doi:10.1021/ie902009k · Cited by 214

Intrinsic Motivation and Reinforcement Learning

Barto, A. G. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_2 · Cited by 243

Adversarial Threshold Neural Computer for Molecular de Novo Design

Lane, E., Asadulaev, A., Vanhaelen, Q., Ivanenkov, Y. A., Aladinskaya, A. V., Aliper, A., & Zhavoronkov, A. A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b01137 · Cited by 226

Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting

Min, M., Wan, X., Xiao, L., Chen, Y., Xia, M., Wu, D., & Dai, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2875926 · Cited by 213

The Habenulo-Raphe Serotonergic Circuit Encodes an Aversive Expectation Value Essential for Adaptive Active Avoidance of Danger

Amo, R., Fredes, F., Kinoshita, M., Aoki, R., Aizawa, H., Agetsuma, M., Aoki, T., Shiraki, T., Kakinuma, H., Matsuda, M., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.035 · Cited by 217

Interplay of approximate planning strategies

Huys, Q. J. M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S. J., Dayan, P., & Roiser, J. P. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1414219112 · Cited by 207

Division of labor in a group of robots inspired by ants' foraging behavior

Labella, T. H., Dorigo, M., & Deneubourg, J. (2006) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/1152934.1152936 · Cited by 212

Decisions from experience: Why small samples?

Hertwig, R., & Pleskac, T. J. (2010) · Cognition · doi:10.1016/j.cognition.2009.12.009 · Cited by 212

Asymptotically Stable Adaptive–Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation

Vamvoudakis, K. G., Miranda, M. F., & Hespanha, J. P. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2487972 · Cited by 213

Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

Hoel, C.-J., Wolff, K., & Laine, L. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569568 · Cited by 257

Reinforcement Learning with Neural Networks for Quantum Feedback

Fösel, T., Tighineanu, P., Weiss, T., & Marquardt, F. (2018) · Max Planck Digital Library · Cited by 234

Triple Dissociation of Information Processing in Dorsal Striatum, Ventral Striatum, and Hippocampus on a Learned Spatial Decision Task

van der Meer, M. A. A., Johnson, A. C., Schmitzer-Torbert, N. C., & Redish, A. D. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.023 · Cited by 225

Model-based reinforcement learning for approximate optimal regulation

Kamalapurkar, R., Walters, P., & Dixon, W. E. (2015) · Automatica · doi:10.1016/j.automatica.2015.10.039 · Cited by 211

Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner

Emmanuel Dupoux, A. (2018) · Cognition · doi:10.1016/j.cognition.2017.11.008 · Cited by 228

When Does Model-Based Control Pay Off?

Kool, W., Cushman, F. A., & Gershman, S. J. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005090 · Cited by 211

Exploration, novelty, surprise, and free energy minimization

Schwartenbeck, P., FitzGerald, T. H. B., Dolan, R. J., & Friston, K. J. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00710 · Cited by 213

Inferences on a multidimensional social hierarchy use a grid-like code

Park, S. A., Miller, D. S., & Boorman, E. D. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00916-3 · Cited by 230

Self-Organization in Small Cell Networks: A Reinforcement Learning Approach

Bennis, M., Perlaza, S. M., Blasco, P., Han, Z., & Poor, H. V. (2013) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2013.060513.120959 · Cited by 203

Stepping out of the box: information processing in the neural networks of the basal ganglia

Bar‐Gad, I., & Bergman, H. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(01)00270-7 · Cited by 209

On the need for on-line learning in brain-computer interfaces

Millán, J. D. R. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2004.1381116 · Cited by 215

Computational noise in reward-guided learning drives behavioral variability in volatile environments

Findling, C., Skvortsova, V., Dromnelle, R., Palminteri, S., & Wyart, V. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0518-9 · Cited by 205

Fuzzy Inference Enabled Deep Reinforcement Learning-Based Traffic Light Control for Intelligent Transportation System

Kumar, N., Rahman, S. S., & Dhakad, N. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2984033 · Cited by 221

Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

Hu, D., Ye, Z., Gao, Y., Ye, Z., Peng, Y., & Yu, N. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3185975 · Cited by 232

ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

Атабекова, Б. Б. (2024) · Habaršy. Pedagogika ġylymdary seriâsy · doi:10.51889/2959-5762.2023.80.4.019

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Review on Reinforcement-concrete Bonded Anchorage

Huang, P., & Cheng, Y. (2022) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2022/v23i12768 · Cited by 2

Variable Ratio Reinforcement and Differential Reinforcement

Franklin, C. W. (1974) · Psychological Reports · doi:10.2466/pr0.1974.34.3.826