Reinforcement Learning: An Introduction

Model-Freeλ-Policy Iteration for Discrete-Time Linear Quadratic Regulation

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Reinforcement learning with artificial microswimmers

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Twenty-Five Lessons from Computational Neuromodulation

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A neurocomputational account of how inflammation enhances sensitivity to punishments versus rewards

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Reinforcement Learning Controller Design for Affine Nonlinear Discrete-Time Systems using Online Approximators

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Uncertainty and Exploration in a Restless Bandit Problem

Speekenbrink, M., & Konstantinidis, E. (2015) · Topics in Cognitive Science · doi:10.1111/tops.12145 · Cited by 177

Error‐related negativity (ERN) and sustained threat: Conceptual framework and empirical evaluation in an adolescent sample

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Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning

Fulton, N., & Platzer, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12107 · Cited by 193

Reinforced Mnemonic Reader for Machine Reading Comprehension

Hu, M., Peng, Y., Huang, Z., Qiu, X., Wei, F., & Zhou, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/570 · Cited by 228

Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution

Liu, L., & Deng, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11630 · Cited by 193

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Chen, X., Wu, C., Chen, T., Zhang, H., Liu, Z., Zhang, Y., & Bennis, M. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2963667 · Cited by 191

Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning

Icarte, R. T., Klassen, T. Q., Valenzano, R., & McIlraith, S. A. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12440 · Cited by 239

Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments

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Theories and computational models of affordance and mirror systems: An integrative review

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Reinforcement learning, conditioning, and the brain: Successes and challenges

Maia, T. V. (2009) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.9.4.343 · Cited by 191

Dopaminergic Genes Predict Individual Differences in Susceptibility to Confirmation Bias

Doll, B. B., Hutchison, K. E., & Frank, M. J. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.6486-10.2011 · Cited by 184

Neural-network-based zero-sum game for discrete-time nonlinear systems via iterative adaptive dynamic programming algorithm

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Integral Q-learning and explorized policy iteration for adaptive optimal control of continuous-time linear systems

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Information Processing in Decision-Making Systems

van der Meer, M. A. A., Kurth-Nelson, Z. L., & Redish, A. D. (2012) · The Neuroscientist · doi:10.1177/1073858411435128 · Cited by 190

Structural color generation: from layered thin films to optical metasurfaces

Wang, D., Liu, Z., Wang, H., Li, M., Guo, L. J., & Zhang, C. (2023) · Nanophotonics · doi:10.1515/nanoph-2022-0063 · Cited by 204

Adjectival vagueness in a Bayesian model of interpretation

Lassiter, D., & Goodman, N. D. (2015) · Synthese · doi:10.1007/s11229-015-0786-1 · Cited by 191

Basis Function Adaptation in Temporal Difference Reinforcement Learning

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Ensemble Algorithms in Reinforcement Learning

Wiering, M., & van Hasselt, H. P. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.920231 · Cited by 194

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Reinkensmeyer, D. J., Burdet, E., Casadio, M., Krakauer, J. W., Kwakkel, G., Lang, C. E., Swinnen, S. P., Ward, N., & Schweighofer, N. (2016) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0148-3 · Cited by 184

Battery Energy Management in a Microgrid Using Batch Reinforcement Learning

Mbuwir, B. V., Ruelens, F., Spiessens, F., & Deconinck, G. (2017) · Energies · doi:10.3390/en10111846 · Cited by 187

Traffic flow optimization: A reinforcement learning approach

Walraven, E., Spaan, M. T. J., & Bakker, B. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.01.001 · Cited by 183

Why do we fall into sync with others? Interpersonal synchronization and the brain’s optimization principle

Koban, L., Ramamoorthy, A., & Konvalinka, I. (2019) · Social Neuroscience · doi:10.1080/17470919.2017.1400463 · Cited by 180

Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning

Carlucho, I., Paula, M. D., Wang, S., Pétillot, Y., & Acosta, G. G. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.05.016 · Cited by 192

An Analysis of the Interaction Between Intelligent Software Agents and Human Users

Burr, C., Cristianini, N., & Ladyman, J. (2018) · Minds and Machines · doi:10.1007/s11023-018-9479-0 · Cited by 205

Sentiment analysis using deep learning approaches: an overview

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An introduction to deep learning in medical physics: advantages, potential, and challenges

Shen, C., Nguyen, D. T. D., Zhou, Z., Jiang, S. B., Dong, B., & Jia, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab6f51 · Cited by 194

Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning

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Sentience and the Origins of Consciousness: From Cartesian Duality to Markovian Monism

Friston, K. J., Wiese, W., & Hobson, J. A. (2020) · Entropy · doi:10.3390/e22050516 · Cited by 186

Real and Fictive Outcomes Are Processed Differently but Converge on a Common Adaptive Mechanism

Fischer, A. G., & Ullsperger, M. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.006 · Cited by 187

Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry

Wiecki, T. V., Poland, J. S., & Frank, M. J. (2015) · Clinical Psychological Science · doi:10.1177/2167702614565359 · Cited by 207

A Pallidus-Habenula-Dopamine Pathway Signals Inferred Stimulus Values

Bromberg-Martin, E. S., Matsumoto, M., Hong, S., & Hikosaka, O. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00158.2010 · Cited by 186

QTCP: Adaptive Congestion Control with Reinforcement Learning

Li, W., Zhou, F., Chowdhury, K. R., et al. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2835758 · Cited by 186

Advances in the computational understanding of mental illness

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A computational reward learning account of social media engagement

Lindström, B., Bellander, M., Schultner, D. T., Chang, A., Tobler, P. N., & Amodio, D. M. (2021) · Nature Communications · doi:10.1038/s41467-020-19607-x · Cited by 185

Dopamine modulation of learning and memory in the prefrontal cortex: insights from studies in primates, rodents, and birds

Puig, M. V., Rose, J., Schmidt, R., & Freund, N. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00093 · Cited by 207

Optimal Control of Wake Up Mechanisms of Femtocells in Heterogeneous Networks

Saker, L., Elayoubi, S. E., Combes, R., & Chahed, T. (2012) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2012.120415 · Cited by 182

Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle

Xu, B., Rathod, D., Zhang, D., Yebi, A., Zhang, X., Li, X., & Filipi, Z. S. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114200 · Cited by 182

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

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Maintenance optimization in industry 4.0

Pinciroli, L., Baraldi, P., & Zio, E. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109204 · Cited by 232

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184

Functional properties of the basal ganglia's re-entrant loop architecture: selection and reinforcement

Redgrave, P., Vautrelle, N., & Reynolds, J. N. J. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.07.060 · Cited by 195

Reinforcement learning algorithms with function approximation: Recent advances and applications

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Feedback Deep Deterministic Policy Gradient With Fuzzy Reward for Robotic Multiple Peg-in-Hole Assembly Tasks

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Linear Tracking MPC for Nonlinear Systems—Part II: The Data-Driven Case

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Trajectory Design for UAV-Based Internet of Things Data Collection: A Deep Reinforcement Learning Approach

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Verification of Markov Decision Processes Using Learning Algorithms

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Reinforcement learning for optimal control of low exergy buildings

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Cost, Benefit, Tonic, Phasic

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A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

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Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

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Reinforcement learning for neural architecture search: A review

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Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning

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Robust Actor–Critic Learning for Continuous-Time Nonlinear Systems With Unmodeled Dynamics

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Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

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Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems

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Hypersensitivity to Reward in Problem Gamblers

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Heterogeneous Coding of Temporally Discounted Values in the Dorsal and Ventral Striatum during Intertemporal Choice

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Natural Language Interfaces to Ontologies: Combining Syntactic Analysis and Ontology-Based Lookup through the User Interaction

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Traffic control and intelligent vehicle highway systems: a survey

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Energy management of hybrid electric bus based on deep reinforcement learning in continuous state and action space

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Deep reinforcement learning-based collision avoidance for an autonomous ship

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Autonomous Vehicles Security: Challenges and Solutions Using Blockchain and Artificial Intelligence

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Learning structures: Predictive representations, replay, and generalization

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Why humans deviate from rational choice

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Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

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Motivational neural circuits underlying reinforcement learning

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Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach

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A robot model of the basal ganglia: Behavior and intrinsic processing

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Accelerating Reinforcement Learning through Implicit Imitation

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Cooperative, hybrid agent architecture for real-time traffic signal control

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Learning Retrosynthetic Planning through Simulated Experience

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StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning

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Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly

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CDDPG: A Deep-Reinforcement-Learning-Based Approach for Electric Vehicle Charging Control

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Online adaptive algorithm for optimal control with integral reinforcement learning

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Perceptual Learning and Decision-Making in Human Medial Frontal Cortex

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Reward-based training of recurrent neural networks for cognitive and value-based tasks

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End-to-End Navigation Strategy With Deep Reinforcement Learning for Mobile Robots

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Deep reinforcement learning for drone navigation using sensor data

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New Potentials for Data‐Driven Intelligent Tutoring System Development and Optimization

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Regularization and feature selection in least-squares temporal difference learning

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Mesolimbic confidence signals guide perceptual learning in the absence of external feedback

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Adaptive History Biases Result from Confidence-Weighted Accumulation of past Choices

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Glider soaring via reinforcement learning in the field

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Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control

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Applied machine learning and artificial intelligence in rheumatology

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Optimized Backstepping Consensus Control Using Reinforcement Learning for a Class of Nonlinear Strict-Feedback-Dynamic Multi-Agent Systems

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Dynamic stochastic electric vehicle routing with safe reinforcement learning

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Toward a Systematic Survey for Carbon Neutral Data Centers

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Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

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The Neurobiology of Reference-Dependent Value Computation

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Advancing Models and Theories for Digital Behavior Change Interventions

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Trusting Humans and Avatars: A Brain Imaging Study Based on Evolution Theory

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Coadaptive Brain–Machine Interface via Reinforcement Learning

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Approximation algorithms for orienteering and discounted-reward TSP

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ИСПОЛЬЗОВAНИЕ ПОТЕНЦИAЛA СОЦИAЛЬНЫХ ПAРТНЕРОВ В ПОДГОТОВКЕ БУДУЩИХ ПЕДAГОГОВ

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

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Susquehanna Chorale Spring Concert "Roots and Wings"

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Review on Reinforcement-concrete Bonded Anchorage

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Variable Ratio Reinforcement and Differential Reinforcement

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