The DOI matches its Crossref record; 185 of the 185 references it cites were found in a registry or an index.
Exploration in deep reinforcement learning: A survey
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“Solving these problems requires a good global exploration strategy [211] that can better balance long-term and short-term environment information [195].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [195]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“While underappreciated in current reasoning literature, the exploration component of RL has long been recognized as a critical driver for emergent behavior in traditional RL (Yue et al., 2025; Silver et al., 2016; Ladosz et al., 2022; Pourcel et al., 2024).”
From Absolute Zero: Reinforced Self-play Reasoning with Zero Data (2025) · cites this paper as Ladosz et al., 2022
In the citing paper
“This subdomain is dedicated to developing AI systems capable of smooth, natural, and context-aware human-computer interactions, by focusing on dialogue modeling, question answering, user intent recognition, and multi-turn context tracking [204], [205], [206], [207].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [206]
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · cited 1×
“impressive performance in Atari games [2], Mujoco simulator [3], controllers tuning [4], autonomous landing [5], self-driving cars [6] and healthcare [7].”
BackgroundReinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · cited 1×
“The most common technique for exploration in reinforcement learning is random exploration [1].”
MethodLabaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · cited 1×
“impressive performance in Atari games [2], Mujoco simulator [3], controllers tuning [4], autonomous landing [5], self-driving cars [6] and healthcare [7].”
BackgroundSimple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733 · cited 1×
“A general overview of reinforcement learning can be found in [23].”
BackgroundProceedings. 1985 IEEE International Conference on Robotics and Automation
(1985) · doi:10.1109/robot.1985 · Cited by 3,692
Research works printed but not linked: 1
- T. Gangwani, Q. Liu, J. Peng, Learning Self-Imitating Diverse Policies, in: 7th International Conference on Learning Representations, ICLR 2019, 2019.
Cited by
Sorted by citations
Review of large vision models and visual prompt engineering
Wang, J., Liu, Z., Zhao, L., Wu, Z., Ma, C., Yu, S., Dai, H., Yang, Q., Liu, Y., Zhang, S., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100047 · Cited by 257
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Hao, J., Yang, T., Tang, H., Bai, C., Liu, J., Meng, Z., Liu, P., & Wang, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3236361 · Cited by 245
“Work [21] is a concurrent work.”
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Hao, J., Yang, T., Tang, H., Bai, C., Liu, J., Meng, Z., Liu, P., & Wang, Z. (2021) · arXiv · doi:10.48550/arxiv.2109.06668 · Cited by 192
“Work [21] is a concurrent work.”
AI for science: Predicting infectious diseases
Zhao, P., Li, S., Cao, Z., Hu, P. J., Wang, J., Xiang, Y., Xie, D. G., & Lu, X. (2024) · Journal of Safety Science and Resilience · doi:10.1016/j.jnlssr.2024.02.002 · Cited by 193
Remote Sensing Image Classification: A Comprehensive Review and Applications
Mehmood, M., Shahzad, A., Zafar, B., Shabbir, A., & Ali, N. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/5880959 · Cited by 160
Metadata
- 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
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 85
- Pages
- 1-22
- ISSN
- 1566-2535 (ISSN-L)
- 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
- 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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