{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:54:23Z","timestamp":1770270863501,"version":"3.49.0"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Defense Science and Technology Innovation Special Zone","award":["1916311LZ001003"],"award-info":[{"award-number":["1916311LZ001003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1109\/tcyb.2020.3028378","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T19:36:52Z","timestamp":1603481812000},"page":"5209-5218","source":"Crossref","is-referenced-by-count":22,"title":["Fast Task Adaptation Based on the Combination of Model-Based and Gradient-Based Meta Learning"],"prefix":"10.1109","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0996-436X","authenticated-orcid":false,"given":"Zhixiong","family":"Xu","sequence":"first","affiliation":[{"name":"Institute of Command and Control Engineering, Army Engineering University, Nanjing, China~"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5198-0932","authenticated-orcid":false,"given":"Xiliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Command and Control Engineering, Army Engineering University, Nanjing, China~"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6997-8504","authenticated-orcid":false,"given":"Lei","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Command and Control Engineering, Army Engineering University, Nanjing, China~"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(02)00228-9"},{"key":"ref4","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Finn"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2019.05.046"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CF.1943-5509.0001292"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09548-8"},{"key":"ref8","volume-title":"RL2: Fast reinforcement learning via slow reinforcement learning","author":"Duan","year":"2016"},{"key":"ref9","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","volume-title":"Advances in Neural Information Processing Systems (NIPS)","author":"Andrychowicz","year":"2016"},{"key":"ref10","volume-title":"A simple neural attentive meta-learner","author":"Mishra","year":"2017"},{"key":"ref11","first-page":"721","article-title":"TADAM: Task dependent adaptive metric for improved few-shot learning","volume-title":"Advances in Neural Information Processing Systems","author":"Oreshkin","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00653"},{"key":"ref13","first-page":"2927","article-title":"Gradient-based meta-learning with learned layerwise metric and subspace","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref14","volume-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref15","first-page":"467","article-title":"Taming MAML: Efficient unbiased meta-reinforcement learning","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Liu"},{"key":"ref16","first-page":"234","article-title":"Bayesian model-agnostic meta-learning","volume-title":"Advances in Neural Information Processing Systems (NIPS)","author":"Kim","year":"2018"},{"key":"ref17","volume-title":"Deep episodic value iteration for model-based meta-reinforcement learning","author":"Hansen","year":"2017"},{"key":"ref18","volume-title":"Learning to adapt in dynamic, real-world environments through meta-reinforcement learning","author":"Nagabandi","year":"2018"},{"key":"ref19","volume-title":"Meta inverse reinforcement learning via maximum reward sharing for human motion analysis","author":"Li","year":"2017"},{"key":"ref20","volume-title":"Toward multimodal model-agnostic meta-learning","author":"Vuorio","year":"2018"},{"key":"ref21","volume-title":"Fast context adaptation via meta-learning","author":"Zintgraf","year":"2018"},{"key":"ref22","volume-title":"Meta reinforcement learning as task inference","author":"Humplik","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref24","first-page":"1329","article-title":"Benchmarking deep reinforcement learning for continuous control","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Duan"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6386109"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/bf00992696"},{"key":"ref27","first-page":"741","article-title":"Trust region policy optimization","volume-title":"Proc. Int. Conf. Mech. Learn. (ICML)","author":"Schulman"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-2766-4_12"},{"key":"ref29","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Demsar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/72.159058"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(97)00161-6"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/9797898\/09238451.pdf?arnumber=9238451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:22:01Z","timestamp":1704842521000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9238451\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":32,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2020.3028378","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6]]}}}