Cited by 865Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot LearningAI-based Problem Solving and Planning

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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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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
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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