Cited by 544Reinforcement Learning in RoboticsAdvanced Bandit Algorithms ResearchArtificial Intelligence in Games

The DOI matches its Crossref record.

An Introduction to Deep Reinforcement Learning

François-Lavet, Vincent, Henderson, Peter, Islam, Riashat, Bellemare, Marc G., Pineau, Joelle·2018·now publishers, Inc. eBooks

DOI 10.1561/9781680835397

Open this paper in CoChat — free account

Abstract

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

Integrity signals

Registry

Crossref · 10.1561/9781680835397

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Sorted by citations

A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 799
Social physics
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
Application of deep reinforcement learning to intrusion detection for supervised problems
Lopez-Martin, M., Carro, B., & Sanchez-Esguevillas, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112963 · Cited by 360
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art
Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 340
Drone Deep Reinforcement Learning: A Review
Azar, A. T., Koubaa, A., Ali Mohamed, N., Ibrahim, H. A., Ibrahim, Z. F., Kazim, M., Ammar, A., Benjdira, B., Khamis, A. M., Hameed, I. A., et al. (2021) · Electronics · doi:10.3390/electronics10090999 · Cited by 314
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 317
Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks
Liang, L., Ye, H., Yu, G., & Li, G. Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2957798 · Cited by 282
A review on deep reinforcement learning for fluid mechanics
Garnier, P., Viquerat, J., Rabault, J., Larcher, A., Kuhnle, A., & Hachem, E. (2021) · Computers & Fluids · doi:10.1016/j.compfluid.2021.104973 · Cited by 311
Deep reinforcement learning in computer vision: a comprehensive survey
Le, N., Rathour, V. S., Yamazaki, K., Luu, K., & Savvides, M. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10061-9 · Cited by 271
Deep reinforcement learning for power system: An overview
Zhang, Z., Zhang, D., & Qiu, R. C. (2019) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2019.00920 · Cited by 287
QTune
Li, G., Zhou, X., Li, S., & Bo, G. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3352063.3352129 · Cited by 281
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
Li, T., Zhu, K., Luong, N. C., Niyato, D. T., Wu, Q., Zhang, Y., & Chen, B. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3160697 · Cited by 281
Direct shape optimization through deep reinforcement learning
Viquerat, J., Rabault, J., Kuhnle, A., Ghraieb, H., Larcher, A., & Hachem, E. (2020) · Journal of Computational Physics · doi:10.1016/j.jcp.2020.110080 · Cited by 262
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments
Padakandla, S. (2021) · ACM Computing Surveys · doi:10.1145/3459991 · Cited by 275
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
Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection
Alavizadeh, H., Alavizadeh, H., & Jang‐Jaccard, J. (2022) · Computers · doi:10.3390/computers11030041 · Cited by 250
Data mining and machine learning methods for sustainable smart cities traffic classification: A survey
Shafiq, M., Tian, Z., Bashir, A. K., Jolfaei, A., & Yu, X. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102177 · Cited by 232
News recommender system: a review of recent progress, challenges, and opportunities
Raza, S., & Ding, C. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10043-x · Cited by 259
Reinforcement learning for iot security: A comprehensive survey
Uprety, A., & Rawat, D. B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3040957 · Cited by 214
Deep neural networks in the cloud: Review, applications, challenges and research directions
Chan, K. Y., Abu-Salih, B., Qaddoura, R., Al-Zoubi, A. M., Palade, V., Pham, D.-S., Ser, J. D., & Muhammad, K. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126327 · Cited by 234
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
Tang, W., Li, B., Barni, M., Li, J., & Huang, J. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3025438 · Cited by 197
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
Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., & Dresp-Langley, B. (2021) · Robotics · doi:10.3390/robotics10010022 · Cited by 188
RL/DRL Meets Vehicular Task Offloading Using Edge and Vehicular Cloudlet: A Survey
Liu, J., Ahmed, M., Mirza, M. A., Khan, W. U., Xu, D., Li, J., Aziz, A., & Han, Z. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3155667 · Cited by 185
Digital Reticular Chemistry
Lyu, H., Ji, Z., Wuttke, S., & Yaghi, O. M. (2020) · Chem · doi:10.1016/j.chempr.2020.08.008 · Cited by 176
(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives
Glavic, M. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.09.008 · Cited by 163
A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data
Cebollada, S., Payá, L., Flores, M., Peidró, A., & Reinoso, Ó. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114195 · Cited by 170
Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review
Arwa, E. O., & Folly, K. A. (2020) · IEEE Access · doi:10.1109/access.2020.3038735 · Cited by 154
DRL-Based Energy-Efficient Resource Allocation Frameworks for Uplink NOMA Systems
Wang, X., Zhang, Y., Shen, R., Xu, Y., & Zheng, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2982699 · Cited by 142
A Review on Computational Intelligence Techniques in Cloud and Edge Computing
Asim, M., Wang, Y., Wang, K., & Huang, P.-Q. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.3007905 · Cited by 141
LORM: Learning to Optimize for Resource Management in Wireless Networks With Few Training Samples
Shen, Y., Shi, Y., Zhang, J., & Letaief, K. B. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2947591 · Cited by 134
Model-Free Quantum Control with Reinforcement Learning
Sivak, V. V., Eickbusch, A., Liu, H., Royer, B., Tsioutsios, I., & Devoret, M. H. (2022) · Physical Review X · doi:10.1103/physrevx.12.011059 · Cited by 130
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
Messaoud, S., Bradai, A., Bukhari, S. H. R., Quang, P. T. A., Ahmed, O. B., & Atri, M. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100314 · Cited by 135
A Hybrid DQN and Optimization Approach for Strategy and Resource Allocation in MEC Networks
Wu, Y.-C., Dinh, T. Q., Fu, Y., Lin, C., & Quek, T. Q. S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3057882 · Cited by 131
Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamforming
Al-Hilo, A., Samir, M., Elhattab, M., Assi, C. M., & Sharafeddine, S. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3141935 · Cited by 123
How Do Machines Learn? Artificial Intelligence as a New Era in Medicine
Koteluk, O., Wartecki, A., Mazurek, S., Kołodziejczak, I., & Mackiewicz, A. A. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010032 · Cited by 129
Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity
Xu, H., Sun, H., Nikovski, D. N., Kitamura, S., Mori, K., & Hashimoto, H. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2903756 · Cited by 119
Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions
Alshehri, A. S., Gani, R., & You, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107005 · Cited by 122
Deep reinforcement learning in transportation research: A review
Farazi, N. P., Zou, B., Ahamed, T., & Barua, L. (2021) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2021.100425 · Cited by 139
An Efficient Hardware Implementation of Reinforcement Learning: The Q-Learning Algorithm
Spanò, S., Cardarilli, G. C., Nunzio, L. D., Fazzolari, R., Giardino, D., Matta, M., Nannarelli, A., & Re, M. (2019) · IEEE Access · doi:10.1109/access.2019.2961174 · Cited by 114
Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving
Likmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M., & Romano, D. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103568 · Cited by 106
A Data-driven Method for Fast AC Optimal Power Flow Solutions via Deep Reinforcement Learning
Zhou, Y., Zhang, B., Xu, C., Lan, T., Diao, R., Shi, D., Wang, Z., & Lee, W. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000522 · Cited by 106
A survey on deep reinforcement learning for audio-based applications
Latif, S., Cuayáhuitl, H., Pervez, F., Shamshad, F., Ali, H. S., & Cambria, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10224-2 · Cited by 126
Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations
Mohammed, M. Q., Kwek, L. C., & Chua, S. C. (2020) · IEEE Access · doi:10.1109/access.2020.3027923 · Cited by 111
Deep Learning for Android Malware Defenses: A Systematic Literature Review
Liu, Y., Tantithamthavorn, C. K., Li, L., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3544968 · Cited by 123
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning
Akbari, M., Abedi, M. R., Joda, R., Pourghasemian, M., Mokari, N., & Erol‐Kantarci, M. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087264 · Cited by 107
Toward Performing Image Classification and Object Detection With Convolutional Neural Networks in Autonomous Driving Systems: A Survey
Turay, T., & Vladimirova, T. (2022) · IEEE Access · doi:10.1109/access.2022.3147495 · Cited by 114
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsPreprint
Schwarzer, M., Anand, A., Goel, R., Hjelm, R. D., Courville, A. C., & Bachman, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05929 · Cited by 246
Beyond artificial intelligence: exploring artificial wisdom
Jeste, D. V., Graham, S. A., Nguyen, T. T., Depp, C. A., Lee, E. E., & Kim, H.-C. (2020) · International Psychogeriatrics · doi:10.1017/s1041610220000927 · Cited by 100
Soft Actor-Critic for Navigation of Mobile Robots
de Jesus, J. C., Kich, V. A., Kolling, A. H., Grando, R. B., Cuadros, M. A. D. S. L., & Gamarra, D. F. T. (2021) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-021-01367-5 · Cited by 99
Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine
Mathema, V. B., Sen, P., Lamichhane, S., Orešič, M., & Khoomrung, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.043 · Cited by 101
An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions
Won, D.-O., Müller, K., & Lee, S. (2020) · Science Robotics · doi:10.1126/scirobotics.abb9764 · Cited by 96
Artificial intelligence, big data, and blockchain in food safety
Zhou, Q., Zhang, H., & Wang, S. (2021) · International Journal of Food Engineering · doi:10.1515/ijfe-2021-0299 · Cited by 109
Artificial intelligence in gastrointestinal endoscopy
Pannala, R., Krishnan, K., Melson, J. E., Parsi, M. A., Schulman, A. R., Sullivan, S. A., Trikudanathan, G., Trindade, A. J., Watson, R. R., Maple, J. T., et al. (2020) · VideoGIE · doi:10.1016/j.vgie.2020.08.013 · Cited by 99
A survey on intrinsic motivation in reinforcement learningPreprint
Aubret, A., Matignon, L., & Hassas, S. (2019) · arXiv.org · doi:10.48550/arxiv.1908.06976 · Cited by 175
QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks
Rischke, J., Sossalla, P., Salah, H., Fitzek, F. H. P., & Martin Reisslein, B. (2020) · IEEE Access · doi:10.1109/access.2020.3025432 · Cited by 95
A deep reinforcement learning approach for rail renewal and maintenance planning
Mohammadi, R., & He, Q. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108615 · Cited by 88
Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects
Muhammad, A. N., Aseere, A. M., Chiroma, H., Shah, H., Gital, A. Y., & Hashem, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05151-8 · Cited by 86
Deep Reinforcement Learning based Intrusion Detection System for Cloud Infrastructure
Sethi, K., Kumar, R., Prajapati, N., & Bera, P. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027452 · Cited by 85
Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning
Vinuesa, R., Lehmkuhl, O., Lozano-Durán, A., & Rabault, J. (2022) · Fluids · doi:10.3390/fluids7020062 · Cited by 96
Integration of Deep Reinforcement Learning and Discrete-Event Simulation for Real-Time Scheduling of a Flexible Job Shop Production
Lang, S., Behrendt, F., Lanzerath, N., Reggelin, T., & Müller, M. (2020) · Winter Simulation Conference · doi:10.1109/wsc48552.2020.9383997 · Cited by 83
Industry 4.0 and prospects of circular economy: a survey of robotic assembly and disassembly
Daneshmand, M., Noroozi, F., Corneanu, C. A., Mafakheri, F., & Fiorini, P. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08389-1 · Cited by 84
Diversity oriented Deep Reinforcement Learning for targeted molecule generation
Pereira, T., Abbasi, M., Ribeiro, B., & Arrais, J. P. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00498-z · Cited by 84
Comprehensive Review on Reaching and Grasping of Objects in Robotics
Marwan, Q. M., Chua, S. C., & Kwek, L. C. (2021) · Robotica · doi:10.1017/s0263574721000023 · Cited by 86
Drones Chasing Drones: Reinforcement Learning and Deep Search Area Proposal
Akhloufi, M. A., Arola, S., & Bonnet, A. (2019) · Drones · doi:10.3390/drones3030058 · Cited by 80
Deep Reinforcement Learning for energy-aware task offloading in join SDN-Blockchain 5G massive IoT edge network
Sellami, B., Hakiri, A., & Yahia, S. B. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.07.024 · Cited by 85
Detecting flaky tests in probabilistic and machine learning applications
Dutta, S., Shi, A., Choudhary, R., Zhang, Z., Jain, A., & Misailovíc, S. (2020) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3395363.3397366 · Cited by 79
Thinking avant la lettre: A Review of 4E Cognition
Carney, J. (2020) · Evolutionary Studies in Imaginative Culture · doi:10.26613/esic.4.1.172 · Cited by 98
A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case
Luis, S. Y., Reina, D. G., & Toral, S. L. (2021) · IEEE Access · doi:10.1109/access.2021.3053348 · Cited by 75
A deep Q-learning portfolio management framework for the cryptocurrency market
Lucarelli, G., & Borrotti, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05359-8 · Cited by 75
A Literature Review of Using Machine Learning in Software Development Life Cycle Stages
Shafiq, S., Mashkoor, A., Mayr‐Dorn, C., & Egyed, A. F. (2021) · IEEE Access · doi:10.1109/access.2021.3119746 · Cited by 79
A Survey on Deep Learning for Steering Angle Prediction in Autonomous Vehicles
Gidado, U. M., Chiroma, H., Aljojo, N., Abubakar, S., Popoola, S. I., & Al-Garadi, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3017883 · Cited by 67
Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
Anderlini, E., Parker, G. G., & Thomas, G. A. (2019) · Applied Sciences · doi:10.3390/app9173456 · Cited by 66
Deep Learning for Medication Recommendation: A Systematic Survey
Ali, Z., Huang, Y., Ullah, I., Feng, J., Deng, C., Thierry, N., Khan, A., Jan, A. U., Shen, X., Rui, W., et al. (2022) · Data Intelligence · doi:10.1162/dint_a_00197 · Cited by 68
Accelerated Derivative-Free Deep Reinforcement Learning for Large-Scale Grid Emergency Voltage Control
Huang, R., Chen, Y., Yin, T., Li, X., Li, A., Tan, J., Yu, W., Liu, Y., & Huang, Q. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3095179 · Cited by 79
AI-Based Resource Allocation in End-to-End Network Slicing Under Demand and CSI Uncertainties
Gharehgoli, A., Nouruzi, A., Mokari, N., Azmi, P., Javan, M. R., & Jorswieck, E. A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3243837 · Cited by 65
Toward the third generation of artificial intelligence
Zhu, J., Su, H., & Zhang, B. (2020) · Scientia Sinica Informationis · doi:10.1360/ssi-2020-0204 · Cited by 61
A deep reinforcement learning approach for the meal delivery problem
Jahanshahi, H., Bozanta, A., Çevik, M., Kavuk, E. M., Tosun, A., Sonuç, S. B., Kosucu, B., & Bener, A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108489 · Cited by 59
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA
Shehab, M., Çiftler, B. S., Khattab, T. M., Abdallah, M. M., & Trinchero, D. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3165590 · Cited by 60
D2MIF: A Malicious Model Detection Mechanism for Federated-Learning-Empowered Artificial Intelligence of Things
Liu, W., Lin, H., Wang, X., Hu, J. W., Kaddoum, G., Piran, M. J., & Alamri, A. M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3081606 · Cited by 58
Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM
Ewald, V., Venkat, R. S., Asokkumar, A., Benedictus, R., Boller, C., & Groves, R. M. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108153 · Cited by 62
Realistic Peer-to-Peer Energy Trading Model for Microgrids using Deep Reinforcement Learning
Chen, T., & Bu, S. (2019) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2019.8905731 · Cited by 57
Deep Reinforcement Learning: A State-of-the-Art Walkthrough
Lazaridis, A., Fachantidis, A., & Vlahavas, I. P. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12412 · Cited by 59
Multi-Agent RL Enables Decentralized Spectrum Access in Vehicular Networks
Xiang, P., Shan, H., Wang, M., Xiang, Z., & Zhu, Z. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3103058 · Cited by 56
A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
Rezwan, S., & Choi, W. (2021) · Electronics · doi:10.3390/electronics10040449 · Cited by 59
Is multiagent deep reinforcement learning the answer or the question? A brief surveyPreprint
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2018) · arXiv (Cornell University) · Cited by 56
DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning
Ma, X., Chen, J., Xia, L., Yang, J. J., Zhao, Q., & Zhou, Z. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.17526 · Cited by 118
Guidance and control of autonomous surface underwater vehicles for target tracking in ocean environment by deep reinforcement learning
Song, D., Gan, W., Yao, P., Zang, W., Zhang, Z., & Qu, X. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.110947 · Cited by 53
Multi-Tenant Provisioning for Quantum Key Distribution Networks With Heuristics and Reinforcement Learning: A Comparative Study
Cao, Y., Zhao, Y., Li, J., Lin, R., Zhang, J., & Chen, J. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.2964003 · Cited by 55
ATMoS: Autonomous Threat Mitigation in SDN using Reinforcement Learning
Akbari, I., Tahoun, E., Salahuddin, M. A., Limam, N., & Boutaba, R. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110426 · Cited by 53
Deep reinforcement learning approach for computation offloading in blockchain-enabled communications systems
Alam, T., Ullah, A., & Benaida, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03663-2 · Cited by 52
An overview of deep reinforcement learning for spectrum sensing in cognitive radio networks
Obite, F., Usman, A. D., & Okafor, E. (2021) · Digital Signal Processing · doi:10.1016/j.dsp.2021.103014 · Cited by 55
Joint Traffic Control and Multi-Channel Reassignment for Core Backbone Network in SDN-IoT: A Multi-Agent Deep Reinforcement Learning Approach
Wu, T., Zhou, P., Wang, B., Li, A., Tang, X., Xu, Z., Chen, K., & Ding, X. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3036456 · Cited by 54
UAV Control for Wireless Service Provisioning in Critical Demand Areas: A Deep Reinforcement Learning Approach
Ho, T. M., Nguyen, K. K., & Cheriet, M. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3088129 · Cited by 50
A shuffled cellular evolutionary grey wolf optimizer for flexible job shop scheduling problem with tree-structure job precedence constraints
Zhu, Z., Zhou, X., Cao, D., & Li, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109235 · Cited by 50
Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster
John, J. M. S., Herwig, T. C., Kafkes, D., Mitrevski, J., Pellico, W. A., Perdue, G., Quintero-Parra, A., Schupbach, B., Seiya, K., Tran, N. V., et al. (2021) · Physical Review Accelerators and Beams · doi:10.1103/physrevaccelbeams.24.104601 · Cited by 54
Grid-Forming Converter and Stability Aspects of Renewable-Based Low-Inertia Power Networks: Modern Trends and Challenges
Alshahrani, S., Khan, K., Abido, M. A., & Khalid, M. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08399-z · Cited by 50
Deep Reinforcement Learning for Cascaded Hydropower Reservoirs Considering Inflow Forecasts
Xu, W., Zhang, X., Peng, A., & Liang, Y. (2020) · Water Resources Management · doi:10.1007/s11269-020-02600-w · Cited by 44
A Collision Avoidance Method Based on Deep Reinforcement Learning
Feng, S., Sebastian, B., & Ben-Tzvi, P. (2021) · Robotics · doi:10.3390/robotics10020073 · Cited by 40

Authors: François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J.
Affiliations: McGill University (CA)
Google (United States) (US)
ORCID: François-Lavet, Vincent
Henderson, Peter
Bellemare, Marc G.
Venue: now publishers, Inc. eBooks
Publisher: now Publishers Inc
Published: 2018
DOI: 10.1561/9781680835397
OpenAlex: W3123212791
Cited by: 544
References: 0
Citations by year: 2026: 5 · 2025: 20 · 2024: 25 · 2023: 83 · 2022: 101 · 2021: 148 · 2020: 116 · 2019: 44 · 2018: 1
FWCI: 26.25
Type: book
Language: en
Open access: closed
Locations: now publishers, Inc. eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
Keywords: deep reinforcement learning, reinforcement learning, deep learning, decision-making, generalization, practical application, robotics, health care, smart grid, finance

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

An Introduction to Deep Reinforcement Learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks