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Transfer Learning in Deep Reinforcement Learning: A Survey
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Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.
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“Even though TL has been widely used in other supervised and unsupervised learning, employing TL can be more complicated for DRL since the knowledge needs to be transferred in the context of MDP [165].”
From Leveraging 3GPP Features and Optimization Techniques for 5G NR-V2X Resource Allocation: A Survey (2025) · cites this paper as [165]
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Liu, R., Zhang, T., Tian, F., Yang, J. M., Yue, H., & Liu, Y. (2024) · International Conference on Electronic Information Engineering and Data Processing (EIEDP) · doi:10.1117/12.3032878 · Cited by 1
Transfer Learning for Dynamical Systems Models via Autoencoders and GANs
Damiani, A., Viera-López, G., Manganini, G., Metelli, A. M., & Restelli, M. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644658 · Cited by 1
Self-Attention Guided Advice Distillation in Multi-Agent Deep Reinforcement Learning
Li, Y., Zhou, S., Hou, Y., Zhou, L., Ge, H., Feng, L., & Wang, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650452 · Cited by 1
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Dagner, T., Krähschütz, M., & Parzeller, R. (2024) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea61579.2024.10665019 · Cited by 1
Few-Shot Specific Emitter Identification via Neural Architecture Search and Deep Transfer Learning
Shi, F., Wang, S., Cai, Z., Peng, Y., Liu, Y., Wang, Y., Adachi, F., & Gui, G. (2024) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2024-spring62846.2024.10683454 · Cited by 1
Metadata
- Authors
- Zhu, Z., Lin, K., Jain, A. K., & Zhou, J.
- Affiliations
- Michigan State University (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-11-01
- Volume
- 45
- Issue
- 11
- Pages
- 13344-13362
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- PMC
- PMC11018366
- OpenAlex
- W4383112908
- Cited by
- 861
- References
- 185
- Citations by year
- 2026: 138 · 2025: 320 · 2024: 230 · 2023: 93
- FWCI
- 73.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2212174, 1749940)
- National Institute on Aging (RF1AG072449)
- Office of Naval Research (N00014-20-1-2382, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- transfer learning, deep reinforcement learning, knowledge transfer, sequential decision making, deep neural network, robotics, game playing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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