Transfer Learning in Deep Reinforcement Learning: A Survey

Human-level control through deep reinforcement learning

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Reinforcement Learning: An Introduction

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A survey on transfer learning

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Generative Adversarial Nets

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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Mastering the game of Go with deep neural networks and tree search

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Dynamic Programming

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Distilling the Knowledge in a Neural Network

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Continuous control with deep reinforcement learning

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Proximal Policy Optimization Algorithms

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Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review

Serrano, S. A., Martínez-Carranza, J., & Sucar, L. E. (2024) · IEEE Access · doi:10.1109/access.2024.3435558 · Cited by 26

Deep reinforcement learning approach for hybrid renewable energy systems optimization

Legrene, I., Wong, T., & Dessaint, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111650 · Cited by 23

Reinforcement Learning Control With Knowledge Shaping

Gao, X. H., Si, J., & Huang, H. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3243631 · Cited by 23

Transfer Learning for Distributed Intelligence in Aerial Edge Networks

Zhang, K., Si, D., Wang, W., Cao, J., & Zhang, Y. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.011.2100061 · Cited by 23

Dynamic robot routing optimization: State–space decomposition for operations research-informed reinforcement learning

Löppenberg, M., Yuwono, S., Diprasetya, M. R., & Schwung, A. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102812 · Cited by 22

Machine Vision and Robotics for Primary Food Manipulation and Packaging: A Survey

Vasudevan, S., Mekhalfi, M. L., Blanes, C., Lecca, M., Poiesi, F., Chippendale, P., Fresnillo, P. M., Mohammed, W. M., & Lastra, J. L. M. (2024) · IEEE Access · doi:10.1109/access.2024.3479781 · Cited by 30

Transfer Learning Applied to Deep Reinforcement Learning for 6G Resource Management in Intra- and Inter-Slice RAN-Edge Domains

Mhatre, S., Adelantado, F., Ramantas, K., & Verikoukis, C. V. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3553407 · Cited by 22

General Board Game Concepts

Piette, É., Stephenson, M., Soemers, D. J. N. J., & Browne, C. (2021) · IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9618990 · Cited by 22

SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe

Depoortere, J., Driesen, J., Suykens, J. A. K., & Kazmi, H. (2025) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.12.003 · Cited by 24

CNN2GNN: How to Bridge CNN With GNN

Jiao, Z., Zhang, H., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3583357 · Cited by 23

Transfer-Based DRL for Task Scheduling in Dynamic Environments for Cognitive Radar

Akbar, S., Adve, R., Ding, Z., & Moo, P. W. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3317819 · Cited by 21

Deep Transfer Learning for Location-Aware Millimeter Wave Beam Selection

Rezaie, S., Amiri, A., de Carvalho, E., & Manchón, C. N. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3091120 · Cited by 20

Frequency Regulation Capacity Offering of District Cooling System: An Intrinsic-Motivated Reinforcement Learning Method

Yu, P., Zhang, H., Song, Y., Hui, H., & Huang, C. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3220732 · Cited by 20

Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making

Hou, Y., Liang, X., Lv, M., Yang, Q., & Li, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106703 · Cited by 21

Policy Fusion Transfer: The Knowledge Transfer for Different Robot Peg-in-Hole Insertion Assemblies

Men, Y., Jin, L., Cui, T., Bai, Y., Li, F., & Song, R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3305709 · Cited by 20

Multi-agent policy transfer via task relationship modeling

Qin, R., Chen, F., Wang, T., Yuan, L., Wu, X., Kang, Y., Zhang, Z., Zhang, C., & Yu, Y. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-3862-1 · Cited by 28

Transfer learning-based nonstationary traffic flow prediction using AdaRNN and DCORAL

Zang, L., Wang, T., Zhang, B., & Li, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125143 · Cited by 21

Evaluation of Transfer Learning and Adaptability in Large Language Models with the GLUE BenchmarkPreprint

Sulaiman, N., & Hamzah, F. (2024) · doi:10.36227/techrxiv.171077989.99407624/v1 · Cited by 20

ChatGPT and generation 'Z': a study on the usage rates of ChatGPT

Babu, M. A., Yusuf, K. M., Eni, L. N., Jaman, S. M. S., & Sharmin, M. R. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101163 · Cited by 19

A Co-Scheduling Framework for DNN Models on Mobile and Edge Devices with Heterogeneous Hardware

Xu, Z., Yang, D., Yin, C., Tang, J., Wang, Y., & Xue, G. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3107424 · Cited by 20

Deep Reinforcement Learning for End-to-End Network Slicing: Challenges and Solutions

Liu, Q., Choi, N., & Han, T. (2022) · IEEE Network · doi:10.1109/mnet.113.2100739 · Cited by 20

Towards practical reinforcement learning for tokamak magnetic control

Tracey, B., Michi, A., Chervonyi, Y., Davies, I., Păduraru, C., Lazic, N., Felici, F., Ewalds, T., Donner, C., Galperti, C., et al. (2024) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2024.114161 · Cited by 28

Lifelong Vehicle Trajectory Prediction Framework Based on Generative Replay

Bao, P., Chen, Z., Wang, J., Dai, D., & Zhao, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300545 · Cited by 19

A Systematic Literature Review of Reinforcement Learning-based Knowledge Graph Research

Tang, Z., Li, T., Wu, D., Liu, J., & Yang, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121880 · Cited by 22

Unveiling dynamics changes: Singular spectrum analysis-based method for detecting concept drift in industrial data streams

Zhang, Y., Liu, Z., Yang, C., Huang, X., Lou, S., Zhang, H., & Yan, D. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111640 · Cited by 18

A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system

Lee, J., Shin, Y., & Moon, I. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103576 · Cited by 18

Parallel Attention-Based Transformer for Channel Estimation in RIS-Aided 6G Wireless Communications

Guo, J., Liu, G., Wu, Q., & Fan, P. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3425433 · Cited by 25

Explainable transfer learning for subsurface soil moisture prediction

Ye, S., Chai, Y., Li, J., Wang, J., Deng, X.-B., & Ran, Q. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133473 · Cited by 17

Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images

Zhang, Y., Ma, X., Li, M., Huang, K., Zhu, J., Wang, M., Wang, X., Wu, M., & Heng, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01756-2 · Cited by 19

SINDy-RL for interpretable and efficient model-based reinforcement learning

Zolman, N., Lagemann, C., Fasel, U., Kutz, J., & Brunton, S. (2024) · Nature Communications · doi:10.1038/s41467-025-65738-4 · Cited by 53

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Sacco, A., Flocco, M., Esposito, F., & Marchetto, G. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3215669 · Cited by 17

Exploitation, exploration and ambidextrous strategies of SMES in accelerating organisational effectiveness

Annamalah, S., Paraman, P., Ahmed, S., Pertheban, T. R., Marimuthu, A. A. L., Venkatachalam, K. R., & Ramayah, T. (2023) · Journal of Global Operations and Strategic Sourcing · doi:10.1108/jgoss-08-2022-0090 · Cited by 17

Data-driven methods for the reduction of energy consumption in warehouses: Use-case driven analysis

Shaer, I., & Shami, A. A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100882 · Cited by 17

A novel method for intersecting machining feature segmentation via deep reinforcement learning

Zhang, H., Wang, W., Zhang, S., Zhang, Y., Zhou, J., Wang, Z., Huang, B., & Huang, R. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102256 · Cited by 17

A high-precision jujube disease spot detection based on SSD during the sorting process

Yin, Z.-B., Liu, F., Geng, H., Xi, Y.-J., Zeng, D.-B., Si, C., & Shi, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296314 · Cited by 17

TraKDis: A Transformer-Based Knowledge Distillation Approach for Visual Reinforcement Learning With Application to Cloth Manipulation

Chen, W., & Rojas, N. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3358750 · Cited by 18

Enhancing Longitudinal Velocity Control With Attention Mechanism-Based Deep Deterministic Policy Gradient (DDPG) for Safety and Comfort

Islam, F., Ball, J. E., & Goodin, C. T. (2024) · IEEE Access · doi:10.1109/access.2024.3368435 · Cited by 17

Automated Left Ventricle Segmentation in Echocardiography Using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment

Balasubramani, M., Sung, C., Hsieh, M., Huang, E. P., Shieh, J.-S., & Abbod, M. (2024) · Electronics · doi:10.3390/electronics13132587 · Cited by 17

SHM-Traffic: DRL and Transfer Learning Based UAV Control for Structural Health Monitoring of Bridges With Traffic

Gadiraju, D. S., Azam, S. E., & Khazanchi, D. (2025) · IEEE Access · doi:10.1109/access.2025.3526193 · Cited by 19

Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic

Meepokgit, T., & Wisayataksin, S. (2024) · Applied Sciences · doi:10.3390/app14177908 · Cited by 16

Sparse attention is all you need for pre-training on tabular data

Isomura, T., Shimizu, R., & Goto, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10698-x · Cited by 20

Transfer Learning for Human Navigation and Triage Strategies Prediction in a Simulated Urban Search and Rescue Task

Guo, Y., Jena, R., Hughes, D., Lewis, M., & Sycara, K. P. (2021) · IEEE RO-MAN · doi:10.1109/ro-man50785.2021.9515526 · Cited by 17

Feature Entropy Adaptive Network for Weak Magnetic Signal Classification

Liu, R., Chang, Q., Wang, Y., & Wang, L. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3326138 · Cited by 16

Metarobotics for Industry and Society: Vision, Technologies, and Opportunities

Kaigom, E. G. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3337380 · Cited by 17

A K-means cluster division of regional photovoltaic power stations considering the consistency of photovoltaic output

Ouyang, J., Chu, L., Chen, X., Zhao, Y., Zhu, X., & Liu, T. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101573 · Cited by 16

Recent Advances in Machine Learning‐Assisted Design and Development of Polymer Materials

Ma, L., Li, W., Yuan, J., Zhu, J., Wu, Y., He, H., & Pan, X. (2025) · Macromolecular Rapid Communications · doi:10.1002/marc.202500361 · Cited by 17

Event-Based Deep Reinforcement Learning for Smoothing Ramp Events in Combined Wind-Storage Energy Systems

Li, J., Wang, X., & Liu, B. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3353859 · Cited by 15

Cooperate or Not Cooperate: Transfer Learning With Multi-Armed Bandit for Spatial Reuse in Wi-Fi

Iturria-Rivera, P. E., Chenier, M., Herscovici, B., Kantarcı, B., & Erol‐Kantarci, M. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3371929 · Cited by 15

Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System

Liu, M., Chai, Y., Li, M., Wang, J., & Zhao, N. (2024) · Remote Sensing · doi:10.3390/rs16122068 · Cited by 15

Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay

Hassani, H., Nikan, S., & Shami, A. A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109147 · Cited by 15

Personalized-Preference-Based Electric Vehicle Charging Recommendation Considering Photovoltaic Consumption: A Transfer Reinforcement Learning Method

Chen, W., Liu, D., & Cao, J. (2024) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2024.3453990 · Cited by 15

Transfer Learning-Driven Electricity Theft Detection in Small-Sample Cases

Liao, W., Zhu, R., Takiddin, A., Tariq, M. H., Ruan, G., Cui, X., & Yang, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3470061 · Cited by 16

Learning to Transfer for Evolutionary Multitasking

Wu, S.-H., Huang, Y., Wu, X., Feng, L., Zhan, Z., & Tan, K. C. (2025) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2025.3561518 · Cited by 15

Is Transfer Learning Helpful for Neural Combinatorial Optimization Applied to Vehicle Routing Problems?

Yaddaden, A., Harispe, S., & Vasquez, M. (2022) · Computing and Informatics · doi:10.31577/cai_2022_1_172 · Cited by 14

Transfer reinforcement learning for multi‐agent pursuit‐evasion differential game with obstacles in a continuous environment

Hu, P., Pan, Q., Zhao, C., & Guo, Y. (2024) · Asian Journal of Control · doi:10.1002/asjc.3328 · Cited by 14

Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment

Lee, D. (2024) · IEEE Access · doi:10.1109/access.2024.3395430 · Cited by 14

Structural damage identification based on Wasserstein Generative Adversarial Network with gradient penalty and dynamic adversarial adaptation network

Li, Z., He, W.-Y., & Ren, W. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111754 · Cited by 14

SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics

Kumar, N., & Kundu, A. (2024) · Sensors · doi:10.3390/s24196300 · Cited by 14

Knowledge guided fuzzy deep reinforcement learning

Qin, P., & Zhao, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125823 · Cited by 14

A review of deep learning in blink detection

Xiong, J., Dai, W., Wang, Q., Dong, X., Ye, B., & Yang, J. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2594 · Cited by 16

Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multimodal-Feature-Based Deep Reinforcement Learning Approach

Bai, Y., Xie, B., Liu, Y., Chang, Z., & Jäntti, R. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3556300 · Cited by 17

Machine learning in sludge Treatment: Applications, mechanisms, and prospects

Wang, D., Xiang, J., Yang, B., Ye, Y., Xiao, J., & He, D. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145710 · Cited by 13

Knee osteoarthritis network: A hybrid transformer-based approach for enhanced detection and grading of knee osteoarthritis

Maqsood, S., Maqsood, N., Shahid, S., Subhan, F. E., Sarwar, M. A., Yousufi, M., Qurthobi, A., Zafar, A., Khan, M. A., Damaševičius, R., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111751 · Cited by 13

Training Mixed-Domain Translation Models via Federated Learning

Passban, P., Roosta, T. G., Gupta, R., Chadha, A., & Chung, C. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.186 · Cited by 15

Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Atashbar, T., & Shi, R. A. (2022) · IMF Working Paper · doi:10.5089/9798400224713.001 · Cited by 13

Transfer learning for process design with reinforcement learning

Gao, Q., Yang, H., Shanbhag, S. M., & Schweidtmann, A. M. (2023) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-443-15274-0.50319-x · Cited by 15

Effective Multi-Agent Communication Under Limited Bandwidth

Yu, L., Wang, Q., Qiu, Y., Wang, J., Zhang, X., & Han, Z. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3339213 · Cited by 13

Reliability evaluation of reinforcement learning methods for mechanical systems with increasing complexity

Manzl, P., Rogov, O., Gerstmayr, J., Mikkola, A., & Orzechowski, G. (2023) · Multibody System Dynamics · doi:10.1007/s11044-023-09960-2 · Cited by 15

Deep Reinforcement Learning in Nonstationary Environments With Unknown Change Points

Liu, Z. Q., Lü, J., Xuan, J., & Zhang, G. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3356981 · Cited by 14

Environmental Prediction Model of Solar Greenhouse Based on Improved Harris Hawks Optimization-CatBoost

Yang, J., Ren, G., Wang, Y., Liu, Q., Zhang, J., Wang, W., Li, L., & Zhang, W. (2024) · Sustainability · doi:10.3390/su16052021 · Cited by 13

DESiRED — Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry

de Almeida, L. C., da Silva, W. R. D., Tavares, T. C., Pasquini, R., Papagianni, C., & Verdi, F. L. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110326 · Cited by 13

Supporting Architectural Decision Making on Training Strategies in Reinforcement Learning Architectures

Ntentos, E., Warnett, S. J., & Zdun, U. (2024) · IEEE International Conference on Software Architecture (ICSA) · doi:10.1109/icsa59870.2024.00017 · Cited by 13

Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks

Luong, H. H., Nguyễn, H. T., & Thai-Nghe, N. (2024) · Journal of Information and Telecommunication · doi:10.1080/24751839.2024.2415033 · Cited by 14

Deep joint subdomain alignment for unsupervised domain adaptation

Zhong, Z., Wang, D., Zhou, Q., & Lan, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125602 · Cited by 12

Large language model augmented syntax-aware domain adaptation method for aspect-based sentiment analysis

Zou, H., & Wang, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129472 · Cited by 17

Artificial intelligence-driven security framework for internet of things-enhanced digital twin networks

Okegbile, S. D., & Gambo, I. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101564 · Cited by 13

A Review of Machine Learning Approaches for Non-Invasive Cognitive Impairment Detection

Alsuhaibani, M., Fard, A. P., Sun, J., Poor, F. F., Pressman, P., & Mahoor, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3555176 · Cited by 20

Advances in van der Waals thermoelectric materials: prospects and challenges

Fronzi, M., Mele, P., Ellis, A. V., & Stampfl, C. (2025) · RSC Applied Interfaces · doi:10.1039/d4lf00383g · Cited by 12

Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning

Shu, Y., Cao, Z., Gao, J., Wang, J., Yu, P. S., & Long, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3319517 · Cited by 14

A load frequency control strategy based on double deep Q-network and upper confidence bound algorithm of multi-area interconnected power systems

Zhang, J., Peng, F., Wang, L., Yang, Y., & Li, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109778 · Cited by 12

Trajectory Tracking Control for Robotic Manipulator Based on Soft Actor–Critic and Generative Adversarial Imitation Learning

Hu, J., Wang, F., Li, X., Qin, Y., Guo, F., & Jiang, M. (2024) · Biomimetics · doi:10.3390/biomimetics9120779 · Cited by 15

TSMixer- and Transfer Learning-Based Highly Reliable Prediction with Short-Term Time Series Data in Small-Scale Solar Power Generation Systems

Lee, Y., & Jeong, J. (2025) · Energies · doi:10.3390/en18040765 · Cited by 12

Fault diagnosis based on deep transfer learning for marine turbocharger

Hu, L., Liu, L., Yang, J., Hu, H., Zheng, C., & Yu, Y. (2025) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2025.110444 · Cited by 12

Multifunctional physical reservoir computing in soft tensegrity robots

Terajima, R., Inoue, K., Nakajima, K., & Kuniyoshi, Y. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0273567 · Cited by 13

Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training

Li, K., Lü, J., Zuo, H., & Zhang, G. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892718 · Cited by 12

Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning

Hu, T., Liao, Q., Liu, Q., & Carle, G. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000763 · Cited by 12

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

Schultheis, M., Straub, D., & Rothkopf, C. A. (2021) · TUbilio (Technical University of Darmstadt) · doi:10.17863/cam.125526 · Cited by 13

Delta Hedging of Derivatives using Deep Reinforcement LearningPreprint

Giurca, A., & Borovkova, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3847272 · Cited by 12

Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning

Muller-Brockhausen, M., Preuss, M., & Plaat, A. (2021) · 2021 IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9619000 · Cited by 11

Reinforcement learning in robotic systems : A review on sim-to-real transfer

Tiwari, R., Khapre, S. P., & Singh, A. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2025.105327 · Cited by 12

Spatial data intelligence and city metaverse: A review

Meng, X., Li, Y., Liu, K., Liu, Y., Yang, B., Song, X., Liao, G., Wang, S., Yu, Z., Chen, L., et al. (2023) · Fundamental Research · doi:10.1016/j.fmre.2023.10.014 · Cited by 19

Multiple QoS Enabled Intelligent Resource Management in Vehicle-to-Vehicle Communication

Deng, Y., Paul, R., & Choi, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3365557 · Cited by 11

Applications of AI/ML in Maritime Cyber Supply Chains

Dı́az, R., Ungo, R., Smith, K., Haghnegahdar, L., Singh, B., & Phuong, T. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.02.140 · Cited by 11

Local instance-based transfer learning for reinforcement learning

Li, X., Ji, W., & Huang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108488 · Cited by 11

Enhancing Image Annotation With Object Tracking and Image Retrieval: A Systematic Review

Fernandes, R., Pessoa, A. C. P., Salgado, M., de Paiva, A. C., Paçal, İ., & Cunha, A. M. T. D. S. (2024) · IEEE Access · doi:10.1109/access.2024.3406018 · Cited by 11

Transfer Optimization for Heterogeneous Drone Delivery and Pickup Problem

Wen, X., Wu, G., Liu, J., & Ong, Y.-S. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3406689 · Cited by 11

Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data

Sadegh‐Zadeh, S., Khezerlouy‐Aghdam, N., Sakha, H., Toufan, M., Behravan, M., Vahedi, A., Rahimi, M., Hosseini, H., Khanjani, S., Bayat, B., et al. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101544 · Cited by 11

A hybrid multilinear-linear subspace learning approach for enhanced person re-identification in camera networks

Gharbi, A. A., Chouchane, A., Ouamane, A., Belabbaci, E. O., Himeur, Y., & Bourennane, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125044 · Cited by 11

Event-Triggered Automatic Parking Control for Unmanned Vehicles Against DoS Attacks

Zhao, H., Zhao, Z., Xu, D., & Yu, H. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3463613 · Cited by 11

Active flow control of square cylinder adaptive to wind direction using deep reinforcement learning

Yan, L., Zhang, X., Song, J., & Hu, G. (2024) · Physical Review Fluids · doi:10.1103/physrevfluids.9.094607 · Cited by 11

Transfer Learning for Reinforcement Learning Domains: A Survey

Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,524

A survey of transfer learning methods for reinforcement learning

Bone, N. (2008) · Western CEDAR (Western Washington University) · Cited by 3

A Survey on Transfer Reinforcement Learning

Wei, J., Lan, Y., Tang, T., & Liu, T. (2025) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11020301 · Cited by 2

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

Glatt, R., da Silva, F. L., Bianchi, R. A. C., & Costa, A. H. R. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_14 · Cited by 2

Multi-source Transfer Learning for Deep Reinforcement Learning

García-Ramírez, J., Morales, E. F., & Escalante, H. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77004-4_13 · Cited by 2