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Amirfarhad FARHADI, Mitra MIRZAREZAEE, Arash SHARIFI, Mohammad TESHNEHLAB. Domain adaptation in reinforcement learning: a comprehensive and systematic study[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Domain adaptation in reinforcement learning: a comprehensive and systematic study",
author="Amirfarhad FARHADI, Mitra MIRZAREZAEE, Arash SHARIFI, Mohammad TESHNEHLAB",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300668"
}
%0 Journal Article
%T Domain adaptation in reinforcement learning: a comprehensive and systematic study
%A Amirfarhad FARHADI
%A Mitra MIRZAREZAEE
%A Arash SHARIFI
%A Mohammad TESHNEHLAB
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300668
TY - JOUR
T1 - Domain adaptation in reinforcement learning: a comprehensive and systematic study
A1 - Amirfarhad FARHADI
A1 - Mitra MIRZAREZAEE
A1 - Arash SHARIFI
A1 - Mohammad TESHNEHLAB
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300668
Abstract: reinforcement learning (RL) has shown significant potential for dealing with complex decision-making problems. However, its performance relies heavily on the availability of a large amount of high-quality data. In many real-world situations, the data distribution in the target domain may differ significantly from that in the source domain, leading to a significant drop in the performance of RL algorithms. domain adaptation (DA) strategies have been proposed to address this issue by transferring knowledge from a source domain to a target domain. However, there have been no comprehensive and in-depth studies to evaluate these approaches. In this paper we present a comprehensive and systematic study of DA in RL. We first introduce the basic concepts and formulations of DA in RL and then review the existing DA methods used in RL. Our main objective was to fill the existing literature gap regarding DA in RL. To achieve this, we conducted a rigorous evaluation of state-of-the-art DA approaches. We aimed to provide comprehensive insights into DA in RL and contribute to advancing knowledge in this field. The existing DA approaches are divided into seven categories based on application domains. The approaches in each category are discussed based on the important data adaptation metrics, and then their key characteristics are described. Finally, challenging issues and future research trends are highlighted to assist researchers in developing innovative improvements.
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