Zero-shot policy generation in lifelong reinforcement learning q
Qian, Yi-Ming1,2; Xiong, Fang-Zhou2,3; Liu, Zhi-Yong1,2,4
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2021-07-25
Volume446Pages:65-73
Corresponding AuthorLiu, Zhi-Yong(zhiyong.liu@ia.ac.cn)
AbstractLifelong reinforcement learning (LRL) is an important approach to achieve continual lifelong learning of multiple reinforcement learning tasks. The two major methods used in LRL are task decomposition and policy knowledge extraction. Policy knowledge extraction method in LRL can share knowledge for tasks in different task domains and for tasks in the same task domain with different system environmental coefficients. However, the generalization ability of policy knowledge extraction method is limited on learned tasks rather than learned task domains. In this paper, we propose a cross-domain lifelong reinforcement learning algorithm with zero-shot policy generation ability (CDLRL-ZPG) to improve generalization ability of policy knowledge extraction method from learned tasks to learned task domains. In experiments, we evaluated CDLRL-ZPG performance on four task domains. And our results show that the proposed algorithm can directly generate satisfactory results without needing a trial and error learning process to achieve zero-shot learning in general. (c) 2021 Elsevier B.V. All rights reserved.
KeywordLifelong reinforcement learning Generalization policy Task domain
DOI10.1016/j.neucom.2021.02.058
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Plan of China[2020AAA0108902] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; NSFC, China[61627808] ; Dongguan core technology research frontier project[2019622101001]
Funding OrganizationNational Key Research and Development Plan of China ; Strategic Priority Research Program of Chinese Academy of Science ; NSFC, China ; Dongguan core technology research frontier project
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000660569000006
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45332
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorLiu, Zhi-Yong
Affiliation1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Meituan, Beijing, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qian, Yi-Ming,Xiong, Fang-Zhou,Liu, Zhi-Yong. Zero-shot policy generation in lifelong reinforcement learning q[J]. NEUROCOMPUTING,2021,446:65-73.
APA Qian, Yi-Ming,Xiong, Fang-Zhou,&Liu, Zhi-Yong.(2021).Zero-shot policy generation in lifelong reinforcement learning q.NEUROCOMPUTING,446,65-73.
MLA Qian, Yi-Ming,et al."Zero-shot policy generation in lifelong reinforcement learning q".NEUROCOMPUTING 446(2021):65-73.
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