An Introduction to Deep Reinforcement Learning

Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage ControlPreprint

Huang, R., Chen, Y., Yin, T., Li, X., Li, A., Tan, J., Yu, W., Liu, Y., & Huang, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.12667 · Cited by 15

The Political Economy of AI: Towards Democratic Control of the Means of PredictionPreprint

Kasy, M. (2023) · SSRN Electronic Journal · doi:10.31235/osf.io/x7pcy · Cited by 18

A UAV Maneuver Decision-Making Algorithm for Autonomous Airdrop Based on Deep Reinforcement Learning

Li, K., Zhang, K., Zhang, Z., Liu, Z., Hua, S., & He, J. (2021) · Sensors · doi:10.3390/s21062233 · Cited by 14

BEHAVE: Behavior-Aware, Intelligent, and Fair Resource Management for Heterogeneous Edge-IoT Systems

AlQerm, I., Wang, J., Pan, J., & Liu, Y. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3068632 · Cited by 15

Hierarchical Memory Decoder for Visual Narrating

Wu, A., Han, Y., Zhao, Z., & Yang, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3020877 · Cited by 14

Traction-assisted colorectal endoscopic submucosal dissection by use of clip and line for a neoplasm involving colonic diverticulum

Iwatsubo, T., Uedo, N., Yamasaki, Y., Takeuchi, Y., & Ando, Y. (2017) · VideoGIE · doi:10.1016/j.vgie.2017.08.012 · Cited by 14

Dynamic Channel Allocation for Multi-UAVs: A Deep Reinforcement Learning Approach

Zhou, X., Lin, Y., Tu, Y., Mao, S., & Dou, Z. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013281 · Cited by 15

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Parák, R., & Matoušek, R. (2021) · MENDEL · doi:10.13164/mendel.2021.1.001 · Cited by 15

DRL-based admission control and resource allocation for 5G network slicing

Chakraborty, S., & Sivalingam, K. M. (2023) · Sadhana · doi:10.1007/s12046-023-02201-4 · Cited by 13

Uncovering the Dynamics in the Application of Machine learning in Computational Finance: A Bibliometric and Social Network Analysis

Ajibade, S.-S. M., Jasser, M. B., Alebiosu, D. O., Al-Hadi, I. A. A.-Q., ALDharhani, G. S., Hassan, F., & Gyamfi, B. A. (2024) · International Journal of Economics and Financial Issues · doi:10.32479/ijefi.16399 · Cited by 13

A Comprehensive Discussion on Deep Reinforcement Learning

Xu, W., Chen, L., & Yang, H. (2021) · International Conference on Communications, Information System and Computer Engineering (CISCE) · doi:10.1109/cisce52179.2021.9445914 · Cited by 15

Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids

Bousnina, D., & Guérassimoff, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95470-3_2 · Cited by 16

In Machines We Trust: Are Robo-Advisers More Trustworthy Than Human Financial Advisers?

Chia, H. X. (2019) · Law Technology and Humans · doi:10.5204/lthj.v1i0.1261 · Cited by 17

Reinforcement learning methods based on GPU accelerated industrial control hardware

Schmidt, A. P., Schellroth, F., Fischer, M., Allimant, L., & Riedel, O. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05848-4 · Cited by 12

Deep Reinforcement Learning-based User Association in Sub6GHz/mmWave Integrated Networks

Dinh, T. H. L., Kaneko, M., Wakao, K., Kawamura, K., Moriyama, T., Abeysekera, H., & Takatori, Y. (2021) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49032.2021.9369600 · Cited by 12

Service Caching for Meteorological Emergency Decision-making in Cloud-Edge Computing

Yan, H., Xu, X., Dai, F., Qi, L., Zhang, X., & Dou, W. (2022) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws55610.2022.00032 · Cited by 12

Integrated adaptive dynamic programming for data-driven optimal controller design

Li, G., Görges, D., & Mu, C. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.095 · Cited by 12

QoS based Deep Reinforcement Learning for V2X Resource Allocation

Bhadauria, S., Shabbir, Z., Roth-Mandutz, E., & Fischer, G. (2020) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom48709.2020.9234960 · Cited by 12

Survey on artificial intelligence algorithms used in industrial robotics

Benotsmane, R., Dudás, L., & Kovács, G. (2020) · Multidiszciplináris Tudományok · doi:10.35925/j.multi.2020.4.23 · Cited by 12

Hierarchical Reinforcement Learning from Demonstration via Reachability-Based Reward Shaping

Gao, X., Liu, J., Wan, B., & An, L. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11632-x · Cited by 11

Intelligent Control of Groundwater in Slopes with Deep Reinforcement Learning

Biniyaz, A., Azmoon, B., & Liu, Z. (2022) · Italian National Conference on Sensors · doi:10.3390/s22218503 · Cited by 10

Deep Learning in Audio Classification

Wang, Y., Wei‐Kocsis, J., Springer, J. A., & Matson, E. T. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-16302-9_5 · Cited by 11

Tourism Route Recommendation Using Reinforcement Learning

Mubarak, M. I., & Baizal, Z. K. A. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126347 · Cited by 11

Dynamic value iteration networks for the planning of rapidly changing UAV swarms

Li, W., Yang, B., Song, G., & Jiang, X. (2021) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900712 · Cited by 11

DeepNavNet: Automated Landmark Localization for Neuronavigation

Edwards, C. A., Goyal, A., Rusheen, A. E., Kouzani, A. Z., & Lee, K. H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.670287 · Cited by 13

Arsenal: Understanding Learning-Based Wireless Video Transport via In-Depth Evaluation

Zhang, H., Zhou, A., Ma, R., Lu, J., & Ma, H. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3105479 · Cited by 11

Active Queue-Management Policies for Undersea Networking via Deep Reinforcement Learning

Forero, P. A., Zhang, P., & Radosevic, D. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9706025 · Cited by 12

Exploring Exploration: Comparing Children with RL Agents in Unified EnvironmentsPreprint

Kosoy, E., Collins, J., Chan, D. M., Huang, S., Pathak, D., Agrawal, P., Canny, J., Alison Gopnik, C., & Hamrick, J. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.02880 · Cited by 23

Automating Turbulence Modeling by Multi-Agent Reinforcement LearningPreprint

Novati, G., de Laroussilhe, H. L., & Koumoutsakos, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.09023 · Cited by 11

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic ReviewPreprint

Ali, A., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2021) · doi:10.36227/techrxiv.14945433 · Cited by 13

Blockchain-Based Zero Trust Supply Chain Security Integrated with Deep Reinforcement LearningPreprint

Ismail, S. S., Moudoud, H., Dawoud, D. W., & Reza, H. (2024) · Preprints.org · doi:10.20944/preprints202403.0714.v1 · Cited by 11

A theory-based and data-driven approach to promoting physical activity through message-based interventions

Catellani, P., Biella, M., Carfora, V., Nardone, A., Brischigiaro, L., Manera, M. R., & Piastra, M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1200304 · Cited by 12

Privacy is not Free: Energy-Aware Federated Learning for Mobile and Edge Intelligence

Yang, W., Zhang, Y., Lim, W. Y. B., Xiong, Z., Jiao, Y., & Jin, J. (2020) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp49889.2020.9299703 · Cited by 10

A Policy Gradient Based Reinforcement Learning Method for Supply Chain Management

Hachaı̈chi, Y., Chemingui, Y., & Affes, M. (2020) · IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) · doi:10.1109/ic_aset49463.2020.9318258 · Cited by 10

Reinforcement Learning Control Scheme for Electrical Submersible Pumps

Chong, J., Kelly, D., Agrawal, S., Nguyen, N. H., & Monzon, M. (2021) · SPE Gulf Coast Section Electric Submersible Pumps Symposium · doi:10.2118/204508-ms · Cited by 10

We need to rethink production technology for meat-packers—the old cutting table is being revived

Hinrichsen, L. L., Wu, H., & Gregersen, K. D. (2022) · Animal Frontiers · doi:10.1093/af/vfab077 · Cited by 10

Learning General Policies with Policy Gradient Methods

Ståhlberg, S., Bonet, B., & Geffner, H. (2023) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2023/63 · Cited by 14

An Intelligent Penetration Test Simulation Environment Construction Method Incorporating Social Engineering Factors

Li, Y., Wang, Y., Xiong, X., Zhang, J., & Yao, Q. (2022) · Applied Sciences · doi:10.3390/app12126186 · Cited by 9

Service caching decision‐making policy for mobile edge computing using deep reinforcement learning

Ke, H., Wang, H., Yang, K., & Sun, H. (2022) · IET Communications · doi:10.1049/cmu2.12546 · Cited by 9

A Framework for DRL Navigation With State Transition Checking and Velocity Increment Scheduling

Dewa, C. K., & Miura, J. (2020) · IEEE Access · doi:10.1109/access.2020.3033016 · Cited by 10

Security Systems for Smart Cities Based on Acoustic Sensors and Machine Learning Applications

Ciaburro, G. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-72065-0_20 · Cited by 9

Using Dueling Double Q-learning for Voltage Regulation in PV-Rich Distribution Networks

Custódio, G. D. O., Ochoa, L. F., Alpcan, T., Trindade, F. C. L., & Rosolen, R. A. G. (2021) · IEEE PowerTech Conference · doi:10.1109/powertech46648.2021.9494903 · Cited by 9

Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

Makinde, A. S., Agbeyangi, A. O., & Nwankwo, W. (2021) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.2021070104 · Cited by 9

Dynamic Antenna Control for HAPS Using Fuzzy Q-Learning in Multi-Cell Configuration

Wada, K., Yang, S., Bouazizi, M., Ohtsuki, T. O., Shibata, Y., Takabatake, W., Hoshino, K., & Nagate, A. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838271 · Cited by 9

Transformer Based Reinforcement Learning For GamesPreprint

Upadhyay, U., Shah, N., Ravikanti, S., & Medhe, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03918 · Cited by 18

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

Coors, V., Pietruschka, D., & Zeitler, B. (2022) · Springer eBooks · doi:10.1007/978-3-030-92096-8 · Cited by 8

Efficient state representation with artificial potential fields for reinforcement learning

Jiang, H., Li, S., Zhang, J., Zhu, Y. S., Xu, X., & Liu, D. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-00995-8 · Cited by 10

FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently

Cong, Z., Shi, B., Li, S., Yang, J., He, Q., & Pei, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3306378 · Cited by 9

Network Intrusion Detection System Using Reinforcement Learning Techniques

Malik, M., & Saini, K. S. (2023) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct58313.2023.10245608 · Cited by 8

Peacekeeping Conditions for an Artificial Intelligence Society

Yamakawa, H. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3020034 · Cited by 8

Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions

Casas, P. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110266 · Cited by 12

Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme

Avrachenkov, K. E., Borkar, V. S., Dolhare, H., & Patil, K. (2021) · Emergence, complexity and computation · doi:10.1007/978-3-030-76928-4_10 · Cited by 8

Robotic Learning From Advisory and Adversarial Interactions Using a Soft Wrist

Hamaya, M., Tanaka, K., Shibata, Y., von Drigalski, F., Nakashima, C., & Ijiri, Y. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3067232 · Cited by 9

One tag, two codes

An, Z., Lin, Q., Zhao, X., Yang, L., Zheng, D., Wu, G., & Chang, S. (2021) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3447993.3448631 · Cited by 8

A Survey of Reinforcement Learning in Intrusion Detection

Utic, Z., & Ramachandran, K. M. (2022) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic53980.2022.9897058 · Cited by 7

Traffic Signal Control Method Based on Modified Proximal Policy Optimization

An, Y., & Zhang, J. (2022) · International Conference on Traffic and Logistic Engineering (ICTLE) · doi:10.1109/ictle55577.2022.9901894 · Cited by 8

Deep Reinforcement Learning For Autonomous Control Of Low Voltage Grids With Focus On Grid Stability In Future Power Grids

Quakernack, L., Kelker, M., & Haubrock, J. (2022) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgt-europe54678.2022.9960416 · Cited by 8

A Reinforcement Learning Approach for an IRS-assisted NOMA NetworkPreprint

Xie, X., Jiao, S., & Ding, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09611 · Cited by 8

Comprehensive Review of Deep Reinforcement Learning Methods and Applications in EconomicsPreprint

Mosavi, A. H., Ghamisi, P., Faghan, Y., Duan, P., Ardabili, S., Salwana, E., & Band, S. S. (2020) · doi:10.31224/osf.io/5qfex · Cited by 10

Reinforcement-Tracking: An End-to-End Trajectory Tracking Method Based on Self-Attention Mechanism

Zhao, G., Chen, Z., & Liao, W. (2024) · International Journal of Automotive Technology · doi:10.1007/s12239-024-00043-5 · Cited by 7

Enhancing Fine-Grained Vulnerability Detection With Reinforcement Learning

Jiang, Y., Qu, Z., Treude, C., Su, X., & Wang, T. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3603400 · Cited by 8

A Sample Efficiency Improved Method via Hierarchical Reinforcement Learning Networks

Chen, Q., Dallas, E., Shahverdi, P., Korneder, J., Rawashdeh, O. A., & Louie, W.-Y. G. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900738 · Cited by 7

Network Intrusion Detection System using Reinforcement learning

Malik, M., & Saini, K. S. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10170630 · Cited by 9

Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

Mochizuki, D., Abiko, Y., Saito, T., Ikeda, D., & Mineno, H. (2019) · Sensors · doi:10.3390/s19071674 · Cited by 7

Ecological management of stochastic systems with long transients

Boettiger, C. (2020) · Theoretical Ecology · doi:10.1007/s12080-020-00477-4 · Cited by 7

Distributed Deep Reinforcement Learning Computations for Routing in a Software-Defined Mobile Ad Hoc Network

Almolaa, P. D. M. Y. K. O. S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i6.3378 · Cited by 7

A deep learning based robot positioning error compensation

Sellami, S., & Klimchik, A. S. (2021) · International Conference Nonlinearity, Information and Robotics (NIR) · doi:10.1109/nir52917.2021.9666097 · Cited by 7

Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments

Kolonin, A. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-93758-4_12 · Cited by 7

Joint Optimization of UAV Trajectory and Task Allocation for Wireless Sensor Network Based on O-RAN Architecture

Pham, C., Nguyen, K. K., & Cheriet, M. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838773 · Cited by 7

Autonomous Vehicle Driving Path Control with Deep Reinforcement Learning

Tiong, T., Saad, I., Teo, K. T. K., & bin Lago, H. (2023) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc57344.2023.10099122 · Cited by 7

Data-Driven Robotic Tactile Grasping for Hyper-Personalization Line Pick-and-Place

Xie, Z., Seng, J. C. Y., Lim, G. W., & Bai, F. (2023) · Actuators · doi:10.3390/act12050192 · Cited by 7

Adaptive inverse deep reinforcement Lyapunov learning control for a floating wind turbine

KhalafAnsar, H. M., & Keighobadi, J. (2023) · Scientia Iranica · doi:10.24200/sci.2023.61871.7532 · Cited by 7

Deep Reinforcement Learning Approach for Augmented Reality Games

Magdy, R., Rashad, S., Hany, M. A. S., Tarek, M., Hassan, M. A., & Mohammed, A. (2021) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc52538.2021.9447671 · Cited by 7

Advanced Double Layered Multi-Agent Systems Based on A3C in Real-Time Path Planning

Lee, D., Kim, J., Cho, K., & Sung, Y. (2021) · Electronics · doi:10.3390/electronics10222762 · Cited by 7

Interpretable UAV Collision Avoidance using Deep Reinforcement LearningPreprint

Thomas, D.-G., Olshanskyi, D., Krueger, K., Wongpiromsarn, T., & Jannesari, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.12254 · Cited by 7

Using Q-learning to Automatically Tune Quadcopter PID Controller Online for Fast Altitude Stabilization

Alrubyli, Y., & Bonarini, A. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9856292 · Cited by 8

Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

Gomrokchi, M., Amin, S., Aboutalebi, H., Wong, A. K. C., & Precup, D. (2023) · IEEE Access · doi:10.1109/access.2023.3270860 · Cited by 6

Anomaly Detection in Structural Health Monitoring with Ensemble Learning and Reinforcement Learning

Huang, N. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150112 · Cited by 6

Dynamic Algorithm for Interference Mitigation Between Cells in Networks Operating in the 250 MHz Band

Carrillo, D., Filho, L. Q. R. D. C., de Medeiros, Á. A. M., Neto, C. L., Figueiredo, F. L., & Rodríguez, D. Z. (2022) · IEEE Access · doi:10.1109/access.2022.3162618 · Cited by 6

Deep-Learning-Driven Secure Offline Blockchain in a Cyber-Physical Social System

Xing, R., Su, Z., Luan, T. H., & Li, R. (2022) · IEEE Network · doi:10.1109/mnet.111.2100700 · Cited by 6

Application of deep reinforcement learning in attacking and protecting structural features-based malicious PDF detector

Jiang, T., Liu, Y., Wu, X., Xu, M., & Cui, X. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.11.015 · Cited by 6

Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests

Xia, C. S., Dutta, S., Misailovíc, S., Marinov, D., & Zhang, L. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00154 · Cited by 7

Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification

Wertz, L., Bogojeska, J., Мирыленка, К., & Kuhn, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.697 · Cited by 7

Multi-advisor Deep Reinforcement Learning for Thermostatically Controlled Heating in Smart Homes

Tittaferrante, A., & Yassine, A. (2020) · IEEE International Conference on Smart Energy Grid Engineering (SEGE) · doi:10.1109/sege49949.2020.9181985 · Cited by 6

An Improved Minimax-Q Algorithm Based on Generalized Policy Iteration to Solve a Chaser-Invader Game

Liu, M., Zhu, Y., & Zhao, D. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207446 · Cited by 6

NLtoPDDL : One-Shot Learning of PDDL Models from Natural Language Process Manuals

Miglani, S., & Yorke‐Smith, N. (2020) · Data Archiving and Networked Services (DANS) · Cited by 6

Deep Learning and Its Application to Function Approximation for MR in Medicine: An Overview

Takeshima, H. (2021) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.rev.2021-0040 · Cited by 7

A Typhoon Center Location Method on Satellite Images Based on Deep Reinforcement Learning

Wang, P., Yang, X., Ji, Z., Hou, J., Wang, C., & Chen, H. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550197 · Cited by 6

Mobility Prediction-Based Wireless Edge Caching Using Deep Reinforcement Learning

Cai, Y., Chen, Y., Ding, M., Cheng, P., & Li, J. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc52777.2021.9580283 · Cited by 6

A Data and Model Parallelism based Distributed Deep Learning System in a Network of Edge Devices

Sen, T., & Shen, H. (2023) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn58024.2023.10230190 · Cited by 6

Distributed Resource Allocation In 5g Networks With Multi-Agent Reinforcement Learning

Menard, J., Al-Habashna, A., Wainer, G., & Boudreau, G. (2022) · Annual Modeling and Simulation Conference (ANNSIM) · doi:10.23919/annsim55834.2022.9859340 · Cited by 5

Multi-granularity coverage criteria for deep reinforcement learning systems

Shi, Y., Yin, B., & Zheng, Z. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112016 · Cited by 5

The Development of a Joint Modelling Framework for Operational Flexibility in Power Systems

VOROPAI, N. I., Rehtanz, C., Kippelt, S., Tomin, N., Häger, U., Efimov, D. N., Kurbatsky, V. G., & Kolosok, I. (2019) · Conference on Electrical Machines, Drives and Power Systems (ELMA) · doi:10.1109/elma.2019.8771685 · Cited by 5

Using Deep Reinforcement Learning to Improve Sensor Selection in the Internet of Things

Rashtian, H., & Gopalakrishnan, S. (2020) · IEEE Access · doi:10.1109/access.2020.2994600 · Cited by 5

Dynamic Resource Management in Next Generation Networks with Dense User Traffic

Aslan, A., Bal, G., & Toker, C. (2020) · IEEE International Black Sea Conference on Communications and Networking · doi:10.1109/blackseacom48709.2020.9235006 · Cited by 5

Battery Management for Automated Warehouses via Deep Reinforcement Learning

Deng, Y., An, B., Qiu, Z., Li, L., Wang, Y., & Xu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64096-5_9 · Cited by 6

Neural Regret-Matching for Distributed Constraint Optimization Problems

Deng, Y., Yu, R., Wang, X., & An, B. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/21 · Cited by 5

Integrated Actor-Critic for Deep Reinforcement Learning

Zheng, J., Kurt, M. N., & Wang, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86380-7_41 · Cited by 5

From Sensable to Sensible Spaces: Enhancing the Sensibility of a Home Office using Stress-Aware Deep Reinforcement Learning in Virtual Environments

Gollob, E., Kyrou, M., Petrantonakis, P. C., & Kompatsiaris, I. Y. (2022) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3491101.3516390 · Cited by 5

Boosting input data sequences generation for testing EFSM-specified systems using deep reinforcement learning

Shu, T., Wu, C., & Ding, Z. (2022) · Information and Software Technology · doi:10.1016/j.infsof.2022.107114 · Cited by 5

Deep Reinforcement Learning: From First Principles to Reasoning ModelsPreprint

Bista, G. (2026) · arXiv (Cornell University) · doi:10.5281/zenodo.21140609

Deep Reinforcement Learning

(2020) · Springer eBooks · doi:10.1007/978-981-15-4095-0 · Cited by 240

DEEP REINFORCEMENT LEARNING: PRINCIPLES AND APPLICATIONS

Bibi, D. M. (2022) · Computer Science Bulletin · doi:10.71465/csb89

Deep Reinforcement Learning for Adaptive Decision-Making in Real-World Applications

Touati, C., & Kemmar, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7082-8.ch005

Deep Reinforcement Learning, a textbookPreprint

Plaat, A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.02135 · Cited by 1