Cited by 528Reinforcement Learning in RoboticsAdvanced Bandit Algorithms ResearchArtificial Intelligence in Games

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Exploration in deep reinforcement learning: A survey

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Ladosz, Pawel, Weng, Lilian, Kim, Minwoo, Oh, Hyondong·2022·Information Fusion

DOI 10.1016/j.inffus.2022.03.003

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Background
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Background
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Background
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“Therefore, the state, action, and reward design will significantly influence DRL’s optimization quality (Ladosz et al., 2022).”
Background
RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation
An, B., Geng, Y., Chen, K., Li, X., Dou, Q., & Dong, H. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610690 · Cited by 17
Hierarchical Diffusion Policy: Manipulation Trajectory Generation via Contact Guidance
Wang, D., Liu, C., Chang, F., & Xu, Y. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3547272 · Cited by 25
“The exploration mechanism enables the agent to learn complex operational skills, but also brings low sample efﬁciency and weak generalization [2].”
Efficient Q-learning hyperparameter tuning using FOX optimization algorithm
Jumaah, M. A., Ali, Y. H., & Rashid, T. A. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104341 · Cited by 16
Path Planning for Agricultural UAVs Based on Deep Reinforcement Learning and Energy Consumption Constraints
Fu, H., Zheng, L., Zhang, W., Feng, Y., Zhu, L., Long, Y., & Li, J. (2025) · Agriculture · doi:10.3390/agriculture15090943 · Cited by 15
Unlocking Terahertz technology with machine learning: A comprehensive review
Cacciari, I., & Ranfagni, A. (2025) · Journal of Applied Physics · doi:10.1063/5.0272428 · Cited by 17
Attention-driven reinforcement learning for multi-satellite collaborative orbital interception strategy solution
Zhang, W., Wang, Y., & Zhang, Y. (2025) · Astrodynamics · doi:10.1007/s42064-024-0246-0 · Cited by 15
“It will guide the training direction of the agent and is of great significance to the convergence speed of the algorithm and the generation of interception strategy [31, 32].”
AID-RL: Active information-directed reinforcement learning for autonomous source seeking and estimation
Li, Z., Chen, W., Yang, J., & Yan, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126281 · Cited by 15
“While there has been a signiﬁcant amount of research on active RL algorithms, such as [23,22,20,15], they are not directly applicable to autonomous seeking and estimation.”
Influential
RLS-DTS: Reinforcement-Learning Linguistic Steganalysis in Distribution-Transformed Scenario
Wang, Y., Zhang, R., & Liu, J. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3310380 · Cited by 15
“Once the observation space is constructed, the agent (steganalyzer) interacts with the observation space [24] to optimize the extraction of steganalysis features.”
Method
End-to-end multi-sensor fusion method based on deep reinforcement learning in UASNs
Zheng, L., Liu, M., Zhang, S., Liu, Z., & Dong, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117904 · Cited by 15
A novel active learning stochastic Kriging metamodel for improving reliability and stability of additive manufacturing processes
Ding, C., Wang, J., Zhang, S., Yang, S., & Ma, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111043 · Cited by 14
Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing
Gyabaah, K. Y., Mahoney, B., Martey, A. K., Yan, C., Mensah, P. F., & Li, G. (2026) · AI Materials · doi:10.3390/aimater1010002 · Cited by 14
A reinforcement double deep Q-network with prioritised experience replay for rolling bearing fault diagnosis
Li, Z., Jiang, H., & Liu, Y. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acf23d · Cited by 16
“Moreover, the efficiency of data utilisation remains a considerable problem, especially in high-dimensional and complex data environments [30].”
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Zhao, A., Wu, Y., Yue, Y., Wu, T., Xu, Q., Yue, Y., Lin, M., Wang, S., Wu, Q., Zheng, Z., et al. (2025) · arXiv · Cited by 13
Machine learning approaches for active queue management: A survey, taxonomy, and future directions
Toopchinezhad, M. P., & Ahmadi, M. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111174 · Cited by 15
Mobile Robot Navigation Based on Noisy N-Step Dueling Double Deep Q-Network and Prioritized Experience Replay
Hu, W., Zhou, Y., & Ho, H. W. (2024) · Electronics · doi:10.3390/electronics13122423 · Cited by 15
“…to exploiting learned knowledge, taking the action with argmax In both the original DQN and DDQN algorithms, the agent utilizes the ϵ -greedy policy [11], choosing the action argmax a ′ Q ( s t , a ′ ; θ ) with a probability of 1 − ϵ and opting for a random action with a probability of ϵ [37,38].”
Method
A digital twin dynamic migration method for industrial mobile robots
Wang, Y., & Zhao, X. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102864 · Cited by 13
Autonomous navigation of UAV in complex environment : a deep reinforcement learning method based on temporal attention
Liu, S., Zou, S., Chang, X., Liu, H., Zhang, L., & Deng, X. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06036-2 · Cited by 13
“This enables the UAV to receive appropriate reward feedback after executing each action, effectively addressing the sparse reward problem in DRL [21].”
Comprehensive review of reinforcement learning for medical ultrasound imaging
Elmekki, H., Islam, S., Alagha, A., Sami, H., Spilkin, A., Zakeri, E., Zanuttini, A., Bentahar, J., Kadem, L., Xie, W., et al. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11268-w · Cited by 14
Evolution Strategies-Guided Deep Reinforcement Learning for Dynamic Hybrid Flow-Shop Scheduling Problem
Luo, L., Yan, X., Wu, Q., & Sheng, V. S. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010141 · Cited by 13
Novelty as a drive of human exploration in complex stochastic environments
Modirshanechi, A., Lin, W.-H., He, X., Herzog, M. H., & Wulfram Gerstner, B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2502193122 · Cited by 19
Optimizing the flexible job shop scheduling problem via deep reinforcement learning with mean multichannel graph attention
Huang, D., Zhao, H. P., Cao, J., Chen, K., & Zhang, L. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113128 · Cited by 12
Ensemble Learning based on CNN and Transformer Models for Leaf Diseases Classification
Li, L.-H., & Tanone, R. (2024) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom60618.2024.10418393 · Cited by 14
“CNN [22] is one of the deep learning based model for solving computer vision problems such as image classification.”
Method
Intelligent Path Planning for UAV Patrolling in Dynamic Environments Based on the Transformer Architecture
Yu, C., Tsai, J., & Chang, Y.-T. (2024) · Electronics · doi:10.3390/electronics13234716 · Cited by 12
Prescribed-time formation tracking in multi-agent systems via reinforcement learning-based hybrid impulsive control with time delays
Liang, Z., Gu, Y., Li, P., & Tao, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126723 · Cited by 12
Achieving Hiding and Smart Anti-Jamming Communication: A Parallel DRL Approach Against Moving Reactive Jammer
Li, Y., Xu, Y., Li, W., Li, G., Feng, Z., Liu, S., Du, J., & Li, X. (2025) · IEEE Transactions on Communications · doi:10.1109/tcomm.2025.3552297 · Cited by 13
“The ε -greedy strategy is a balancing act between exploration [21] and exploitation, which is crucial for learning optimal policies.”
Toward Optimized In‐Memory Reinforcement Learning: Leveraging 1/f Noise of Synaptic Ferroelectric Field‐Effect‐Transistors for Efficient Exploration
Kim, J., Shin, W., Yim, J., Kwon, D., Kwon, D., & Lee, J. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202300763 · Cited by 12
Artificial intelligence in disaster management: achievements, challenges, and prospects
Xu, J., Nair, D. J., & Waller, S. T. (2025) · Natural Hazards · doi:10.1007/s11069-025-07667-5 · Cited by 14

Authors: Ladosz, P., Weng, L., Kim, M., & Oh, H.
Affiliations: OpenAI (United States) (US)
Ulsan National Institute of Science and Technology (KR)
Corresponding author: Oh, Hyondong
ORCID: Kim, Minwoo
Oh, Hyondong
Venue: Information Fusion
Publisher: Elsevier BV
Published: 2022-09
Volume: 85
Pages: 1-22
ISSN: 1566-2535 (ISSN-L)
DOI: 10.1016/j.inffus.2022.03.003
OpenAlex: W4220747123
Cited by: 525
References: 185
Citations by year: 2026: 112 · 2025: 198 · 2024: 149 · 2023: 59 · 2022: 7
FWCI: 33.97
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Information Fusion (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Research Explorer (The University of Manchester) (submittedVersion, cc-by-nc-nd)
Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
arXiv (Cornell University)
Funders: Ministry of Science, ICT and Future Planning (2020M3C1C1A01082375, 2020R1F1A1049066)
National Research Foundation of Korea
Ministry of Education (2020R1A6A1A03040570)
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: safe exploration, random exploration, reward learning

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