An Introduction to Deep Reinforcement Learning

Artificial Intelligence and Cyber-Physical Systems: A Review and Perspectives for the Future in the Chemical Industry

Oliveira, L. M. C., Dias, R. O. M., Rebello, C. M., Martins, M. A. F., Rodrigues, A. E., Ribeiro, A. M., & Nogueira, I. B. R. (2021) · AI · doi:10.3390/ai2030027 · Cited by 47

Real-time digital twin machine learning-based cost minimization model for renewable-based microgrids considering uncertainty

Pan, M., Xing, Q., Chai, Z., Zhao, H., Sun, Q., & Duan, D. (2023) · Solar Energy · doi:10.1016/j.solener.2023.01.006 · Cited by 43

Review of Learning-Based Robotic Manipulation in Cluttered Environments

Mohammed, M. Q., Kwek, L. C., Chua, S. C., Al-Dhaqm, A., Nahavandi, S., Eisa, T. A. E., Miskon, M. F., Al‐Mhiqani, M. N., Ali, A., Abaker, M., et al. (2022) · Sensors · doi:10.3390/s22207938 · Cited by 45

The Societal Implications of Deep Reinforcement Learning

Whittlestone, J., Arulkumaran, K., & Crosby, M. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12360 · Cited by 47

A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading

Lucarelli, G., & Borrotti, M. (2019) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-19823-7_20 · Cited by 41

Deep-VFog: When Artificial Intelligence Meets Fog Computing in V2X

Rihan, M., Elwekeil, M., Yang, Y., Huang, L., Chen, X., & Selim, M. M. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3009998 · Cited by 43

Faults in deep reinforcement learning programs: a taxonomy and a detection approach

Nikanjam, A., Morovati, M. M., Khomh, F., & Braiek, H. B. (2021) · Automated Software Engineering · doi:10.1007/s10515-021-00313-x · Cited by 41

A novel energy management method based on Deep Q Network algorithm for low operating cost of an integrated hybrid system

Sanaye, S., & Sarrafi, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.04.055 · Cited by 40

Building a Lane Merge Coordination for Connected Vehicles Using Deep Reinforcement Learning

Nassef, O., Sequeira, L., Salam, E., & Mahmoodi, T. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017931 · Cited by 39

Automatic P2P Energy Trading Model Based on Reinforcement Learning Using Long Short-Term Delayed Reward

Kim, J.-G., & Lee, B. (2020) · Energies · doi:10.3390/en13205359 · Cited by 42

Bibliometric Study on the Use of Machine Learning as Resolution Technique for Facility Layout Problems

Burggräf, P., Wagner, J., & Heinbach, B. (2021) · IEEE Access · doi:10.1109/access.2021.3054563 · Cited by 43

Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: a review

Carpio, L. E., Sanz, Y., Gozalbes, R., & Barigye, S. J. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10277-5 · Cited by 42

Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

Isufaj, R., Omeri, M., & Piera, M. À. (2022) · Applied Sciences · doi:10.3390/app12020610 · Cited by 37

Load Balancing in Cellular Networks: A Reinforcement Learning Approach

Attiah, K. M., Banawan, K., Gaber, A., Elezabi, A. Y., Seddik, K. G., Gadallah, Y., & Abdullah, K. (2020) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc46108.2020.9045699 · Cited by 36

Synergizing medical imaging and radiotherapy with deep learning

Shan, H., Jia, X., Yan, P., Li, Y., Paganetti, H., & Wang, G. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab869f · Cited by 37

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) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/2d5g7 · Cited by 43

Learning Control for Air Conditioning Systems via Human Expressions

Wei, Q., Li, T., & Liu, D. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3001849 · Cited by 35

On the current state of deep learning for news recommendation

Amir, N., Jabeen, F., Ali, Z., Ullah, I., Jan, A. U., & Kefalas, P. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10191-8 · Cited by 36

Intelligent Control Strategy for Transient Response of a Variable Geometry Turbocharger System Based on Deep Reinforcement Learning

Hu, B., Yang, J., Li, J., Li, S., & Bai, H. (2019) · Processes · doi:10.3390/pr7090601 · Cited by 36

REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

Galkin, B., Fonseca, E., Amer, R., DaSilva, L. A., & Dusparić, I. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3126536 · Cited by 34

Mobility Load Management in Cellular Networks: A Deep Reinforcement Learning Approach

Alsuhli, G. H., Banawan, K., Attiah, K. M., Elezabi, A. Y., Seddik, K. G., Gaber, A., Zaki, M., & Gadallah, Y. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3107458 · Cited by 34

Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources

Tomin, N., Жуков, А. В., & Domyshev, A. (2019) · EPJ Web of Conferences · doi:10.1051/epjconf/201921701016 · Cited by 33

Standoff tracking control of underwater glider to moving target

Zang, W., Yao, P., & Song, D. (2021) · Applied Mathematical Modelling · doi:10.1016/j.apm.2021.09.011 · Cited by 33

Adaptive Leader-Follower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning

Zhou, Y., Lü, F., Pu, G., Ma, X., Sun, R., Chen, H., & Li, X. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967561 · Cited by 31

Reinforcement Learning Approaches to Power Management in Grid-tied Microgrids: A Review

Arwa, E. O., & Folly, K. A. (2020) · Clemson University Power Systems Conference · doi:10.1109/psc50246.2020.9131138 · Cited by 32

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

Rahbari, D., Alam, M. M., Moullec, Y. L., & Jenihhin, M. (2021) · IEEE Access · doi:10.1109/access.2021.3104117 · Cited by 31

Tactical Decision-Making for Autonomous Driving Using Dueling Double Deep Q Network With Double Attention

Zhang, S., Wu, Y., Ogai, H., Inujima, H., & Tateno, S. (2021) · IEEE Access · doi:10.1109/access.2021.3127105 · Cited by 31

Towards out of distribution generalization for problems in mechanics

Yuan, L., Park, H. S., & Lejeune, E. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115569 · Cited by 34

Multi-UAV-assisted computation offloading in DT-based networks: A distributed deep reinforcement learning approach

Shi, J., Li, C., Guan, Y., Cong, P., & Li, J. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.07.041 · Cited by 30

Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods

Lu, J., Meng, Y., Timmermans, H. J. P., & Zhang, A. (2021) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2021.03.006 · Cited by 32

A systematic review of federated learning incentive mechanisms and associated security challenges

Ali, A., Ilahi, I., Qayyum, A., Mohammed, I., Al‐Fuqaha, A., & Qadir, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100593 · Cited by 34

Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis

Alhashmi, S. M., Hashem, M., & Al-Qudah, I. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200299 · Cited by 44

Intelligent Control of a Wind Turbine based on Reinforcement Learning

Tomin, N., Kurbatsky, V. G., & Guliyev, H. B. (2019) · Conference on Electrical Machines, Drives and Power Systems (ELMA) · doi:10.1109/elma.2019.8771645 · Cited by 29

Reward criteria impact on the performance of reinforcement learning agent for autonomous navigation

Dayal, A., Cenkeramaddi, L. R., & Jha, A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109241 · Cited by 33

Bias in Reinforcement Learning: A Review in Healthcare Applications

Smith, B., Khojandi, A., & Vasudevan, R. K. (2023) · ACM Computing Surveys · doi:10.1145/3609502 · Cited by 29

The neurobiology of deep reinforcement learning

Gershman, S. J., & Ölveczky, B. P. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.021 · Cited by 28

Optimizing blood–brain barrier permeation through deep reinforcement learning for de novo drug design

Pereira, T., Abbasi, M., Oliveira, J. L., Ribeiro, B., & Arrais, J. P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab301 · Cited by 31

Adaptive Streaming of 360-Degree Videos with Reinforcement Learning

Park, S., Hoai, M., Bhattacharya, A., & Das, S. R. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00188 · Cited by 27

Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

Liu, C., Kampen, E.-J. V., & de Croon, G. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160324 · Cited by 29

Multiagent-based deep reinforcement learning framework for multi-asset adaptive trading and portfolio management

Cheng, L.-C., & Sun, J.-S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127800 · Cited by 26

Analysing deep reinforcement learning agents trained with domain randomisation

Dai, T., Arulkumaran, K., Gerbert, T., Tukra, S., Behbahani, F., & Bharath, A. A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.005 · Cited by 28

Optimal policy learning for COVID-19 prevention using reinforcement learning

Uddin, M. I., Shah, S. A. A., Al‐Khasawneh, M. A., Alarood, A. A., & Alsolami, E. (2020) · Journal of Information Science · doi:10.1177/0165551520959798 · Cited by 29

A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids

Ghasemi, A., Shojaeighadikolaei, A., Jones, K. R., Hashemi, M., Bardas, A. G., & Ahmadi, R. (2020) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm47815.2020.9302981 · Cited by 26

Reinforcement Learning for Radiotherapy Dose Fractioning Automation

Moreau, G., François-Lavet, V., Desbordes, P., & Macq, B. M. M. (2021) · Biomedicines · doi:10.3390/biomedicines9020214 · Cited by 27

Heuristic and deep reinforcement learning-based PID control of trajectory tracking in a ball-and-plate system

Okafor, E., Udekwe, D., Ibrahim, Y., Mu’azu, M. B., & Okafor, E. G. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2020.1833137 · Cited by 25

SecBoost: Secrecy-Aware Deep Reinforcement Learning Based Energy-Efficient Scheme for 5G HetNets

Sharma, H., Kumar, N., & Tekchandani, R. K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3235429 · Cited by 25

Intelligent Reflecting Surface Enabled Fingerprinting-Based Localization With Deep Reinforcement Learning

Wang, Y., Ho, I. W., Zhang, S., & Wang, Y. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3275581 · Cited by 24

Q-Learning ADR Agent for LoRaWAN Optimization

Carvalho, R. F., AL-Tam, F., & Correia, N. (2021) · IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) · doi:10.1109/iaict52856.2021.9532518 · Cited by 24

Interference Mitigation for 5G-Connected UAV using Deep Q-Learning Framework

Warrier, A., Al–Rubaye, S., Panagiotakopoulos, D., İnalhan, G., & Tsourdos, A. (2022) · IEEE/AIAA Digital Avionics Systems Conference (DASC) · doi:10.1109/dasc55683.2022.9925817 · Cited by 24

Free-Lunch Saliency via Attention in Atari Agents

Nikulin, D., Ianina, A., Aliev, V., & Nikolenko, S. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00522 · Cited by 26

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving CarsPreprint

Emuna, R., Borowsky, A., & Biess, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.04218 · Cited by 31

Modeling Production Scheduling Problems as Reinforcement Learning Environments based on Discrete-Event Simulation and OpenAI Gym

Lang, S., Kuetgens, M., Reichardt, P., & Reggelin, T. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.08.093 · Cited by 23

Empirical Deep Hedging

Mikkilä, O., & Kanniainen, J. (2021) · Quantitative Finance · doi:10.1080/14697688.2022.2136037 · Cited by 23

Spatio-temporal hard attention learning for skeleton-based activity recognition

Nikpour, B., & Armanfard, N. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109428 · Cited by 23

Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach

Pinto-Ríos, J., Calderón, F., Leiva, A., Hermosilla, G., Beghelli, A., Bórquez‐Paredes, D., Lozada, A., Jara, N., Olivares, R., & Saavedra, G. (2023) · Complexity · doi:10.1155/2023/4140594 · Cited by 25

Graph convolutional network-based reinforcement learning for tasks offloading in multi-access edge computing

Leng, L., Li, J., Shi, H., & Zhu, Y. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11130-5 · Cited by 23

A Hybrid Recommendation for Music Based on Reinforcement Learning

Wang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47426-3_8 · Cited by 22

Optimizing Discharge Efficiency of Reconfigurable Battery With Deep Reinforcement Learning

Jeon, S., Kim, J., Ahn, J., & Cha, H. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.3012230 · Cited by 22

End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning

Capasso, A. P., Maramotti, P., Dell’Eva, A., & Broggi, A. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575135 · Cited by 23

Domain adversarial transfer for cross-domain and task-constrained grasp pose detection

Jing, X., Qian, K., Xu, X., Bai, J., & Zhou, B. (2021) · Robotics Auton. Syst. · doi:10.1016/j.robot.2021.103872 · Cited by 20

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Li, C., Wu, S., Li, Z., Zhang, Y., Zhang, L., & Gomes, L. (2022) · Electronics · doi:10.3390/electronics11091390 · Cited by 22

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

Hao, Z., Maio, F. D., & Zio, E. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109231 · Cited by 22

An Anonymous Authenticated Group Key Agreement Scheme for Transfer Learning Edge Services Systems

Meng, X., Liang, W., Xu, Z., Li, K., Khan, M. K., & Kui, X. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3657292 · Cited by 21

A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem

Di, Y., Deng, L., & Zhang, L. (2024) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2024.101764 · Cited by 21

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

Hlophe, M. C., & Maharaj, B. T. (2021) · IEEE Access · doi:10.1109/access.2021.3104099 · Cited by 21

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

Priya, B., & Malhotra, J. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03606-x · Cited by 22

OSSP-PTA: An Online Stochastic Stepping Policy for PTA on Reinforcement Learning

Niu, D., Dong, Y., Jin, Z., Zhang, C., Li, Q., & Sun, C. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3251731 · Cited by 21

A Reinforcement Learning Framework to Discover Natural Flavor Molecules

Queiroz, L. P., Rebello, C. M., Costa, É. A., Santana, V. V., Rodrigues, B. C. L., Rodrigues, A. E., Ribeiro, A. M., & Nogueira, I. B. R. (2023) · Foods · doi:10.3390/foods12061147 · Cited by 21

Smart urban windcatcher: Conception of an AI-empowered wind-channeling system for real-time enhancement of urban wind environment

Zhang, B., Li, C. Y., Kikumoto, H., Niu, J., & Tse, K. T. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111357 · Cited by 22

Spatial Hard Attention Modeling via Deep Reinforcement Learning for Skeleton-Based Human Activity Recognition

Nikpour, B., & Armanfard, N. (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2023.3250120 · Cited by 22

Coding Electronic Health Records with Adversarial Reinforcement Path Generation

Wang, S., Ren, P., Chen, Z., Ren, Z., Nie, J., Ma, J., & de Rijke, M. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401135 · Cited by 21

Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning

Shateri, M., Messina, F. J., Piantanida, P., & Labeau, F. (2020) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgt-europe47291.2020.9248831 · Cited by 20

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) · Frenxiv (OSF Preprints) · doi:10.31226/osf.io/53esy · Cited by 24

Cognitive Radar Principles for Defence and Security Applications

Bockmair, M., Fischer, C., Letsche-Nuesseler, M., Neumann, C., Schikorr, M., & Steck, M. (2019) · IEEE Aerospace and Electronic Systems Magazine · doi:10.1109/maes.2019.2953802 · Cited by 19

Deep Learning: Current State

Salas, J., Vidal, F. D. B., & Martinez-Trinidad, F. (2019) · IEEE Latin America Transactions · doi:10.1109/tla.2019.9011537 · Cited by 20

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning

Ibrahim, M., Alsheikh, A., & Elhafiz, R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2017366 · Cited by 19

Deep Reinforcement Learning-Based Optimization for End-to-End Network Slicing With Control- and User-Plane Separation

Wang, Y., Zhao, L., Chu, X., Song, S., Deng, Y., Nallanathan, A., & Liang, K. F. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3191882 · Cited by 19

Wireless Network Abnormal Traffic Detection Method Based on Deep Transfer Reinforcement Learning

Xia, Y., Dong, S., Peng, T., & Wang, T. (2021) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn53354.2021.00083 · Cited by 17

A Hybrid Machine Learning-based Control Strategy for Autonomous Driving Optimization

Reda, A., Benotsmane, R., Bouzid, A., & Vásárhelyi, J. (2023) · Acta Polytechnica Hungarica · doi:10.12700/aph.20.9.2023.9.10 · Cited by 18

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

Cui, F., Lin, X., Zhang, R., & Yang, Q. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.1042882 · Cited by 17

Multi-Objective Coordinated Optimization for UAV Charging Scheduling in Intelligent Aerial-Ground Perception Networks

Zhou, Y. F., Cheng, X., Huaguang, S., Jin, Z., Ning, N., & Fu-qiang, L. (2023) · Chinese Journal of Electronics · doi:10.23919/cje.2022.00.334 · Cited by 17

A multi‐agent deep reinforcement learning approach for traffic signal coordination

Hu, T., & Li, Z. (2024) · IET Intelligent Transport Systems · doi:10.1049/itr2.12521 · Cited by 24

A Computing Budget Allocation Method for Minimizing EV Charging Cost Using Uncertain Wind Power

Jiang, Z., Jia, Q., & Guan, X. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2995914 · Cited by 17

Optimal Policy Learning for Disease Prevention Using Reinforcement Learning

Khan, Z. A., Feng, Z., Uddin, M. I., Mast, N., Shah, S. A. A., Imtiaz, M., Al‐Khasawneh, M. A., & Mahmoud, M. (2020) · Scientific Programming · doi:10.1155/2020/7627290 · Cited by 17

A Unifying Framework for Reinforcement Learning and Planning

Moerland, T. M., Broekens, J., Plaat, A., & Jonker, C. M. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.908353 · Cited by 38

Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks

Saraiva, J. V., Braga, I. M., Monteiro, V. F., Lima, F. R. M., Maciel, T. F., Freitas, W. C., & Cavalcanti, F. R. P. (2020) · Journal of Communication and Information Systems · doi:10.14209/jcis.2020.7 · Cited by 18

UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG

Luo, Y., Song, J. L., Zhao, K., & Liu, Y. (2022) · Applied Sciences · doi:10.3390/app12031618 · Cited by 16

Learning Generalized Policies without Supervision Using GNNs

Ståhlberg, S., Bonet, B., & Geffner, H. (2022) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2022/49 · Cited by 16

Surface path tracking method of autonomous surface underwater vehicle based on deep reinforcement learning

Song, D., Gan, W., Yao, P., Zang, W., & Qu, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08009-3 · Cited by 17

Optimal control of renewable energy communities with controllable assets

Aittahar, S., de Villena, M. M., Derval, G., Castronovo, M., Boukas, I., Gemine, Q., & Ernst, D. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.879041 · Cited by 16

Sharing of Energy Among Cooperative Households Using Distributed Multi-Agent Reinforcement Learning

Ebell, N., Gütlein, M., & Pruckner, M. (2019) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2019.8905520 · Cited by 16

Neural Combinatorial Optimization for Production Scheduling with Sequence-Dependent Setup Waste

Gannouni, A., Самсонов, В., Behery, M., Meisen, T., & Lakemeyer, G. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282869 · Cited by 16

A Quantitative Paradigm for Decision-Making in Precision Oncology

Engelhardt, D., & Michor, F. (2021) · Trends in cancer · doi:10.1016/j.trecan.2021.01.006 · Cited by 16

Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points

Galkin, B., Omoniwa, B., & Dusparić, I. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838809 · Cited by 16

Fault-Tolerant Routing Algorithm for Mesh based NoC using Reinforcement Learning

Samala, J., Takawale, H., Chokhani, Y., Bhanu, P. V., & Soumya, J. (2020) · International Symposium on VLSI Design and Test (VDAT) · doi:10.1109/vdat50263.2020.9190340 · Cited by 15

Traffic signal control using a cooperative EWMA-based multi-agent reinforcement learning

Qiao, Z., Ke, L., & Wang, X. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03643-9 · Cited by 16

SAC-AP: Soft Actor Critic based Deep Reinforcement Learning for Alert Prioritization

Chavali, L., Gupta, T., & Saxena, P. (2022) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec55065.2022.9870423 · Cited by 18

Power Consumption Optimization Using Gradient Boosting Aided Deep Q-Network in C-RANs

Luo, Y., Yang, J., Xu, W., Wang, K., & Renzo, M. D. (2020) · IEEE Access · doi:10.1109/access.2020.2978935 · Cited by 15

An Agent-based Model for Resource Provisioning and Task Scheduling in Cloud Computing Using DRL

Oudaa, T., Gharsellaoui, H., & Ahmed, S. B. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.09.154 · Cited by 18

UAV pursuit using reinforcement learning

Bonnet, A., & Akhloufi, M. A. (2019) · doi:10.1117/12.2520310 · Cited by 15

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

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