{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T04:46:53Z","timestamp":1768106813422,"version":"3.49.0"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004921","name":"Oceanic Interdisciplinary Program of Shanghai Jiao Tong University","doi-asserted-by":"publisher","award":["SL2022ZD106"],"award-info":[{"award-number":["SL2022ZD106"]}],"id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004921","name":"Oceanic Interdisciplinary Program of Shanghai Jiao Tong University","doi-asserted-by":"publisher","award":["SL2023ZD206"],"award-info":[{"award-number":["SL2023ZD206"]}],"id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2023YFC2811103"],"award-info":[{"award-number":["2023YFC2811103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42406183"],"award-info":[{"award-number":["42406183"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tase.2025.3577984","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T17:37:16Z","timestamp":1749490636000},"page":"16547-16559","source":"Crossref","is-referenced-by-count":1,"title":["Cognitive UAV Tracking: Leveraging DRL and Hybrid Curriculum Learning for Target Reacquisition"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3161-381X","authenticated-orcid":false,"given":"Jiaqing","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Submarine Geoscience, the Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; and the School of Oceanography, Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Baichuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}]},{"given":"Lan","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Submarine Geoscience, the Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; and the School of Oceanography, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8968-9902","authenticated-orcid":false,"given":"Ze","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Engineering, Cardiff University, Cardiff, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5788-6573","authenticated-orcid":false,"given":"Changyun","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-8282","authenticated-orcid":false,"given":"Zheng","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Submarine Geoscience, the Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; and the School of Oceanography, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2603528"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3355061"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600238CM"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2024.3384570"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/app9132661"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574721001181"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2702198"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2856526"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TRA.2002.802218"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700422"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-012-9737-y"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3390\/app10155064"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00298"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2927838"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"ref16","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3417400"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10161323"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/biomimetics7040197"},{"key":"ref20","first-page":"1230","article-title":"Finite sample analysis of average-reward td learning and q-learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Zhang"},{"key":"ref21","first-page":"46","article-title":"Off-policy training for truncated TD (\u03bb) boosted soft actor-critic","volume-title":"PRICAI 2021: Trends in Artificial Intelligence","author":"Huang","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2024.3391942"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2024.3407412"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2025.3546079"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3166531"},{"issue":"1","key":"ref26","first-page":"7382","article-title":"Curriculum learning for reinforcement learning domains: A framework and survey","volume":"21","author":"Narvekar","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2934906"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3048662"},{"issue":"1","key":"ref29","first-page":"6818","article-title":"Intrinsically motivated goal exploration processes with automatic curriculum learning","volume":"23","author":"Forestier","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/app12063153"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1023\/b:visi.0000029664.99615.94"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01438"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref34","first-page":"1587","article-title":"Addressing function approximation error in actor-critic methods","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Fujimoto"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03503-6"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-019-01073-3"},{"key":"ref37","first-page":"1861","article-title":"Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Haarnoja"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574719000316"},{"key":"ref39","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. ICML","volume":"30","author":"Maas"},{"key":"ref40","first-page":"692","article-title":"True online TD(\u03bb)","volume-title":"Proc. 31st Int. Conf. Mach. Learn.","author":"Seijen"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3488519"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127958"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2024.3425755"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636309"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3146911"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/10839176\/11028099.pdf?arnumber=11028099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:15:00Z","timestamp":1750745700000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11028099\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/tase.2025.3577984","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}