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Conventional proportional\u2013integral\u2013derivative (PID) algorithms require frequent control parameter adjustments under varying voyage conditions, which increases operational and experimental costs. To address this issue, a multiple line\u2010of\u2010sight guidance law integrated with a deep reinforcement learning control framework is proposed. This framework enables seamless switching among guidance modes, such as waypoint following, path following, and trajectory tracking, to achieve optimal attitude control. For comprehensive control of roll, pitch, yaw, and longitudinal velocity, an augmented\u2010twin delayed deep deterministic policy gradient (A\u2010TD3) algorithm streamlines the training of the control agent. It enables adaptation to large\u2010range attitude variations using small\u2010scale training data, thereby reducing computational costs for diverse missions. Simulations demonstrate the efficacy of the proposed approach: A\u2010TD3 improves training speed by 30.8% while mitigating issues such as excessive rudder motion, poor operability, and high energy consumption across different missions. The attitude control experiments on the X\u2010AUV prototype validate that A\u2010TD3's control performance with PID method.<\/jats:p>","DOI":"10.1002\/aisy.202400991","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T16:19:25Z","timestamp":1751300365000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Model\u2010Free Deep Reinforcement Learning with Multiple Line\u2010of\u2010Sight Guidance Laws for Autonomous Underwater Vehicles Full\u2010Attitude and Velocity Control"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0840-5806","authenticated-orcid":false,"given":"Chengren","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute of Noise &amp; Vibration Naval University of Engineering  Wuhan 430033 China"},{"name":"National Key Laboratory on Ship Vibration &amp; Noise  Wuhan 430033 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changgeng","family":"Shuai","sequence":"additional","affiliation":[{"name":"Institute of Noise &amp; Vibration Naval University of Engineering  Wuhan 430033 China"},{"name":"National Key Laboratory on Ship Vibration &amp; Noise  Wuhan 430033 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanshuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Noise &amp; Vibration Naval University of Engineering  Wuhan 430033 China"},{"name":"National Key Laboratory on Ship Vibration &amp; Noise  Wuhan 430033 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Noise &amp; Vibration Naval University of Engineering  Wuhan 430033 China"},{"name":"National Key Laboratory on Ship Vibration &amp; Noise  Wuhan 430033 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Intelligent Game and Decision Laboratory Academy of Military Sciences  Beijing 100091 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuChen","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Noise &amp; Vibration Naval University of Engineering  Wuhan 430033 China"},{"name":"National Key Laboratory on Ship Vibration &amp; Noise  Wuhan 430033 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3321033"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116714"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06476-8"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/1729881418806745"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0076857"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.111453"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.056"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/jmse9091020"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2018.05.016"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11771-015-2884-0"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.114056"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.113976"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2645699"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2021.102960"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2020.566037"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","unstructured":"J.Parras S.Zazo inICASSP 2021IEEE Int. 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