ArticleIEEE transactions on pattern analysis and machine intelligence2023
Transfer Learning in Deep Reinforcement Learning: A Survey.
Article in IEEE transactions on pattern analysis and machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 649 citations in OpenAlex.
- AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.Frontiers in pharmacology · 2026Review
- SINDy-RL for interpretable and efficient model-based reinforcement learning.Nature communications · 2025Article
- Integrating transfer learning with scalogram analysis for blood pressure estimation from PPG signals.Scientific reports · 2025Article
- Reproducing the Few-Shot Learning Capabilities of the Visual Ventral Pathway Using Vision Transformers and Neural Fields.Brain sciences · 2025Article
- Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis.BMC cancer · 2025Article
- Research on Robot Obstacle Avoidance and Generalization Methods Based on Fusion Policy Transfer Learning.Biomimetics (Basel, Switzerland) · 2025Article
- Applying Reinforcement Learning to Protect Deep Neural Networks from Soft Errors.Sensors (Basel, Switzerland) · 2025Article
- Generalist medical foundation model improves prostate cancer segmentation from multimodal MRI images.NPJ digital medicine · 2025Article
- Diagnosis of thyroid nodules using ultrasound images based on deep learning features: online dynamic nomogram and gradient-weighted class activation mapping.Quantitative imaging in medicine and surgery · 2025Article
- Deep Reinforcement Learning of Mobile Robot Navigation in Dynamic Environment: A Review.Sensors (Basel, Switzerland) · 2025Review
- A Novel Lightweight Algorithm for Sonar Image Recognition.Sensors (Basel, Switzerland) · 2025Article
- Spatial data intelligence and city metaverse: A review.Fundamental research · 2025Review
- Improving TMJ Diagnosis: A Deep Learning Approach for Detecting Mandibular Condyle Bone Changes.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- From Task Distributions to Expected Paths Lengths Distributions: Value Function Initialization in Sparse Reward Environments for Lifelong Reinforcement Learning.Entropy (Basel, Switzerland) · 2025Article
- Using reinforcement learning in genome assembly: in-depth analysis of a Q-learning assembler.Frontiers in bioinformatics · 2025Article
- Constructing ancestral recombination graphs through reinforcement learning.Frontiers in genetics · 2025Article
- A review of deep learning in blink detection.PeerJ. Computer science · 2025Review
- Trajectory Tracking Control for Robotic Manipulator Based on Soft Actor-Critic and Generative Adversarial Imitation Learning.Biomimetics (Basel, Switzerland) · 2024Article
- Investigating Transfer Learning in Noisy Environments: A Study of Predecessor and Successor Features in Spatial Learning Using a T-Maze.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.