Transfer Learning in Deep Reinforcement Learning: A Survey

Zhu, Zhuangdi; Lin, Kaixiang; Jain, Anil K.; Zhou, Jiayu

doi:10.1109/tpami.2023.3292075

Cited by 864Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot LearningAI-based Problem Solving and Planning

Transfer Learning in Deep Reinforcement Learning: A Survey

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Zhu, Zhuangdi, Lin, Kaixiang, Jain, Anil K., Zhou, Jiayu·2023·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2023.3292075PMC PMC11018366

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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.

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Employing transfer learning can be more complicated for DRL since the knowledge needs to be transferred in the context of MDP.
“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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Control of Memory, Active Perception, and Action in MinecraftPreprint
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Learning Invariant Feature Spaces to Transfer Skills with Reinforcement LearningPreprint
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A theoretical and empirical analysis of Expected Sarsa
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Actor-Mimic: Deep Multitask and Transfer Reinforcement LearningPreprint
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Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
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A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
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“We refer readers to [58] for a more focused survey on meta RL.”
Method
Automatic Goal Generation for Reinforcement Learning AgentsPreprint
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The Cross-Entropy Method for Optimization
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Equivalence Between Policy Gradients and Soft Q-LearningPreprint
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Dynamic potential-based reward shaping
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A Survey of Multi-Task Deep Reinforcement Learning
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Manifold alignment without correspondence
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Model-Based Value Estimation for Efficient Model-Free Reinforcement LearningPreprint
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Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation LearningPreprint
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Learning to Search Better Than Your TeacherPreprint
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Principled methods for advising reinforcement learning agents
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Deep Successor Reinforcement LearningPreprint
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The Microbial Genetic Algorithm
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Reinforcement Learning from Imperfect DemonstrationsPreprint
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Improving alignment of dialogue agents via targeted human judgements
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Moving average rules, volume and the predictability of security returns with feedforward networks
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Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay
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Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog ModelPreprint
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Goal-Conditioned Reinforcement Learning: Problems and Solutions
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“Each individual goal g ∼ G can be differentiated by its reward function r ( s t , a t , g ) , hence the objective for GCRL becomes maximizing the expected return over the distribution of goals: J ( π ) := IE ( s t ,a t ) ∼ μ π ,g ∼G [ (cid:5) t γ t r ( s, a, g )] [15].”
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Reinforcement Learning in Healthcare: A SurveyPreprint
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Expressing Arbitrary Reward Functions as Potential-Based Advice
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Policy Optimization with Demonstrations
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Learning to Compose Neural Networks for Question AnsweringPreprint
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Bayesian Multi-Task Reinforcement Learning
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Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment
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Multi-Task Reinforcement Learning with Soft ModularizationPreprint
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Multimodal image registration with deep context reinforcement learning
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Adaptive Treatment Strategies in Practice
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Transfer in variable-reward hierarchical reinforcement learning
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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy ImprovementPreprint
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Learning from Limited Demonstrations
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Distilling Policy DistillationPreprint
Czarnecki, W. M., Pascanu, R., Osindero, S., Jayakumar, S. M., Świrszcz, G., & Jaderberg, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02186 · Cited by 72 · see citing works
Alignment-based transfer learning for robot models
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Belief Reward Shaping in Reinforcement Learning
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Online learning of shaping rewards in reinforcement learning
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Kickstarting Deep Reinforcement Learning
Schmitt, S., Hudson, J. J., Zidek, A., Osindero, S., Doersch, C., Czarnecki, W. M., Leibo, J. Z., Kuttler, H., Zisserman, A., Simonyan, K., et al. (2018) · arXiv · doi:10.48550/arxiv.1803.03835 · Cited by 67 · see citing works
Boosted Bellman Residual Minimization Handling Expert Demonstrations
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Decoupling Dynamics and Reward for Transfer LearningPreprint
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Disagreement-Regularized Imitation Learning
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Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
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Advantages and Limitations of using Successor Features for Transfer in Reinforcement LearningPreprint
Lehnert, L., Tellex, S., & Littman, M. L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00102 · Cited by 38 · see citing works
Multi-Hop Knowledge Graph Reasoning with Reward ShapingPreprint
Lin, X., Socher, R., & Xiong, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.10568 · Cited by 36 · see citing works
State Alignment-based Imitation LearningPreprint
Liu, F., Ling, Z., Mu, T., & Su, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10947 · Cited by 35 · see citing works
Domain Adaptive Imitation LearningPreprint
Kim, K., Gu, Y., Song, J., Zhao, S., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00105 · Cited by 35 · see citing works
Representation Matters: Offline Pretraining for Sequential Decision MakingPreprint
Yang, M., & Nachum, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05815 · Cited by 34 · see citing works
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert DemonstrationsPreprint
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A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
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Off-Policy Imitation Learning from ObservationsPreprint
Zhu, Z., Lin, K., Dai, B., & Zhou, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.13185 · Cited by 27 · see citing works
Self-citation
Hierarchical Decision Making In Electricity Grid ManagementPreprint
Dalal, G., Gilboa, E., & Mannor, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.01840 · Cited by 26 · see citing works
Learning Shaping Rewards in Model-based Reinforcement Learning
Grześ, M., & Kudenko⋆, D. (2009) · Cited by 26 · see citing works
Direct Policy Iteration with Demonstrations
Chemali, J. J., & Lazaric, A. (2015) · LillOA (Université de Lille (University Of Lille)) · Cited by 23 · see citing works
Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering
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Reinforcement Learning Transfer via Common Subspaces
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Model-Predictive Control via Cross-Entropy and Gradient-Based OptimizationPreprint
Bharadhwaj, H., Xie, K., & Shkurti, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.08763 · Cited by 22 · see citing works
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
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Goal-Conditioned Reinforcement Learning: Problems and SolutionsPreprint
Liu, M., Zhu, M., & Zhang, W. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.08299 · Cited by 13 · see citing works
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 · cited 1× · see citing works
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Method
Incorporating Prior Knowledge into Q-Learning for Drug Delivery Individualization
Gaweda, A. E., Muezzinoglu, M. K., Aronoff, G. R., Jacobs, A. A., Żurada, J. M., & Brier, M. E. (2006) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2005.40 · Cited by 11 · see citing works
Imitation-Regularized Offline LearningPreprint
Ma, Y., Wang, Y.-X., Balakrishnan, & Narayanaswamy (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.04723 · Cited by 10 · see citing works
Disentangled Skill Embeddings for Reinforcement LearningPreprint
Petangoda, J. C., Pascual-Díaz, S., Adam, V., Vrancx, P., & Grau-Moya, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09223 · Cited by 10 · see citing works
Improving Policy Optimization with Generalist-Specialist Learning
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Learning Sparse Rewarded Tasks from Sub-Optimal DemonstrationsPreprint
Zhu, Z., Lin, K., Dai, B., & Zhou, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00530 · Cited by 9 · see citing works
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Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes
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Towards Interpretable Explanations for Transfer Learning in Sequential Tasks
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Accelerating Drugs Discovery with Deep Reinforcement Learning
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Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic SettingPreprint
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Reinforcement Learning for Primary care e Appointment Scheduling
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CAD2RL: Real Single-Image Flight without a Single Real Image
Sadeghi, F., & Levine, S. (2016) · arXiv · doi:10.48550/arxiv.1611.04201
Policy Transfer using Reward Shaping
Brys, T., Harutyunyan, A., Taylor, M. E., & Nowé, A. (2015) · International Joint Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/junf6077
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
Ding, W., Lin, H., Li, B., & Zhao, D. (2022) · arXiv · doi:10.48550/arxiv.2207.09081
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Su, P.-H., Vandyke, D., Gasic, M., Mrksic, N., Wen, T.-H., & Young, S. (2015) · arXiv · doi:10.48550/arxiv.1508.03391
Potential-based difference rewards for multiagent reinforcement learning
Devlin, S., Yliniemi, L., Kudenko, D., & Tumer, K. (2014) · International Joint Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/adib2802
Reinforcement learning transfer via sparse coding
Ammar, H. B., Tuyls, K., Taylor, M. E., Driessens, K., & Weiss, G. (2012) · International Joint Conference on Autonomous Agents and Multiagent Systems · doi:10.65109/rsvm8502

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“That is, methods incorporating prior knowledge5 such as transfer from related environments (Zhu et al., 2021); strong inductive biases (Zambaldi et al.”
BackgroundMethod
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
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Reinforcement Learning Control of a Forestry Crane Manipulator
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Methods of Transfer Learning for Multiclass Hair Disease Categorization
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GRL-PS: Graph Embedding-Based DRL Approach for Adaptive Path Selection
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Flexible recommendation for optimizing the debt collection process based on customer risk using deep reinforcement learning
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Feature-Level Knowledge Distillation for Place Recognition Based on Soft-Hard Labels Teaching Paradigm
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Integrating transfer learning and spectroscopy for enhanced pork spoilage assessment using correlation analysis
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Continual Deep Reinforcement Learning for Decentralized Satellite Routing
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Deep Transfer Learning Based on Hybrid Swin Transformers With LSTM for Intrusion Detection Systems in IoT Environment
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State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections
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Transformer-Based Distributed Task Offloading and Resource Management in Cloud-Edge Computing Networks
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Interfacial Insights and Remediation Strategies through Molecular Dynamics Probing of Nanoparticle Interactions in Cementitious Matrices: A Review
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Distributed Deep Learning in Open CollaborationsPreprint
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Transfer Learning Deep EDC-Tabnet for Predicting End-Point of BOF Steelmaking Process with Small Samples
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Toward discretization-consistent closure schemes for large eddy simulation using reinforcement learning
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A transfer-based reinforcement learning collaborative energy management strategy for extended-range electric buses with cabin temperature comfort consideration
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Few-Shot Specific Emitter Identification Leveraging Neural Architecture Search and Advanced Deep Transfer Learning
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Bone, N. (2008) · Western CEDAR (Western Washington University) · Cited by 3 · see citing works
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 · see citing works
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 1 · see citing works
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 · see citing works

Authors: Zhu, Z., Lin, K., Jain, A. K., & Zhou, J.
Affiliations: Michigan State University (US)
ORCID: Zhu, Zhuangdi
Lin, Kaixiang
Jain, Anil K.
Zhou, Jiayu
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2023-11-01
Volume: 45
Issue: 11
Pages: 13344-13362
ISSN: 0162-8828 (ISSN-L)
2160-9292
1939-3539
DOI: 10.1109/tpami.2023.3292075
PMC: PMC11018366
OpenAlex: W4383112908
Cited by: 861
References: 185
Citations by year: 2026: 138 · 2025: 320 · 2024: 230 · 2023: 93 · 2022: 38 · 2021: 39 · 2020: 2 · 2019: 1
FWCI: 73.62
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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

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Transfer Learning in Deep Reinforcement Learning: A Survey

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