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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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Equivalence Between Policy Gradients and Soft Q-LearningPreprint
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Reinforcement learning from demonstration through shaping
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Principled methods for advising reinforcement learning agents
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The Microbial Genetic Algorithm
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Improving alignment of dialogue agents via targeted human judgements
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Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay
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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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Policy Optimization with Demonstrations
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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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Distilling Policy DistillationPreprint
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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy ImprovementPreprint
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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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Dynamic Reward Shaping: Training a Robot by Voice
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Learning from Limited Demonstrations
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Decoupling Dynamics and Reward for Transfer LearningPreprint
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Belief Reward Shaping in Reinforcement Learning
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Kickstarting Deep Reinforcement Learning
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Policy Transfer using Reward Shaping
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State Alignment-based Imitation LearningPreprint
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Enhancing Q-Learning for Optimal Asset Allocation
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Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert DemonstrationsPreprint
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Universal Successor Features ApproximatorsPreprint
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Advantages and Limitations of using Successor Features for Transfer in Reinforcement LearningPreprint
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Multi-Hop Knowledge Graph Reasoning with Reward ShapingPreprint
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Domain Adaptive Imitation LearningPreprint
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A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
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Self-citationDirect Policy Iteration with Demonstrations
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Hierarchical Decision Making In Electricity Grid ManagementPreprint
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Learning Shaping Rewards in Model-based Reinforcement Learning
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Reinforcement Learning Transfer via Common Subspaces
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Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
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Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
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“…for robotics domains, including domain generalization and zero-shot transfer, which is a favored application field of DRL as we discussed in Sec VI. [71] conducted extensive database search and summarized benchmarks for evaluating TL algorithms in RL. [72] surveyed recent progress in multi-task RL.”
MethodIncorporating Prior Knowledge into Q-Learning for Drug Delivery Individualization
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Disentangled Skill Embeddings for Reinforcement LearningPreprint
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Improving Policy Optimization with Generalist-Specialist Learning
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Self-citationTowards Interpretable Explanations for Transfer Learning in Sequential Tasks
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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
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Policy Transfer using Reward Shaping
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Ding, W., Lin, H., Li, B., & Zhao, D. (2022) · arXiv · doi:10.48550/arxiv.2207.09081
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Potential-based difference rewards for multiagent reinforcement learning
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Model-based Reinforcement Learning: A Survey
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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
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