{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:42:16Z","timestamp":1779360136456,"version":"3.51.4"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173107"],"award-info":[{"award-number":["62173107"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1109\/lra.2023.3282792","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T17:59:59Z","timestamp":1685987999000},"page":"4418-4425","source":"Crossref","is-referenced-by-count":24,"title":["On Deep Recurrent Reinforcement Learning for Active Visual Tracking of Space Noncooperative Objects"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9106-9542","authenticated-orcid":false,"given":"Dong","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-2220","authenticated-orcid":false,"given":"Guanghui","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0307-5641","authenticated-orcid":false,"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8198-5267","authenticated-orcid":false,"given":"Ligang","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-09997-9"},{"key":"ref35","first-page":"1928","article-title":"Asynchronous methods for deep reinforcement learning","author":"mnih","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054625"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1177\/0954410019866282"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3108623"},{"key":"ref37","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017"},{"key":"ref14","first-page":"3286","article-title":"End-to-end active object tracking via reinforcement learning","author":"luo","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICETCI51973.2021.9574071"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01694-6"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3153312"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2017.11.006"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2016.02.003"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2020.10.019"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2514\/1.A34838"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2952590"},{"key":"ref39","first-page":"1","article-title":"Continuous control with deep reinforcement learning","author":"lillicrap","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2021.103799"},{"key":"ref38","first-page":"1","article-title":"Deep recurrent Q-learning for partially observable MDPs","author":"hausknecht","year":"0","journal-title":"Proc AAAI Fall Symp Ser"},{"key":"ref19","first-page":"12782","article-title":"Towards distraction-robust active visual tracking","author":"zhong","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561258"},{"key":"ref24","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","article-title":"Space noncooperative object active tracking benchmark","author":"zhou","year":"2022"},{"key":"ref26","first-page":"19884","article-title":"Reinforcement learning with augmented data","author":"laskin","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICETCI51973.2021.9574071"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3107153"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3150866"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3101988"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2021.07.023"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2514\/1.A32813"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2014.03.002"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.paerosci.2019.01.004"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2017.2671558"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2022.3211246"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2019.09.001"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.actaastro.2019.09.002"},{"key":"ref40","first-page":"1","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"zagoruyko","year":"0","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/10153452\/10143684.pdf?arnumber=10143684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T17:46:51Z","timestamp":1689702411000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10143684\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":40,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/lra.2023.3282792","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8]]}}}