{"id":3517,"date":"2025-08-06T13:26:12","date_gmt":"2025-08-06T12:26:12","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=3517"},"modified":"2026-04-13T10:01:14","modified_gmt":"2026-04-13T09:01:14","slug":"27-3-3-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-3-3-en\/","title":{"rendered":"27.3.3 En"},"content":{"rendered":"\n

Development of an unmanned vehicle course control system based on reinforcement learning<\/strong><\/h1>\n\n\n\n

A.E. Ushakov, M.M. Stebulyanin, M.A. Shereuzhev, F.V. Devyatkin<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong>.<\/em><\/strong> At present, there is a growing development of autonomous transportation, driven by the need to improve road safety, reduce collisions, and enhance the efficiency of logistics operations. This trend is also influenced by increasing complexity in road conditions and challenges related to vehicle navigation and control, which make traditional control algorithms insufficient in terms of quality and effectiveness. Aim. The objective of this research is to develop an intelligent system that enables an autonomous vehicle to independently control its course. The autonomous agent (a vehicle model) learns to navigate and follow a predefined trajectory using reinforcement learning through interaction with a simulation environment, based on the Actor-Critic method. Materials and Methods<\/strong>. In this work, the Stable-Baselines 3 (SB3) library built on the PyTorch framework was used to implement and train the reinforcement learning model. The DonkeyCar simulator served as the training environment. To improve the speed and efficiency of training, a denoising autoencoder algorithm was applied to extract the region of interest (ROI). Results<\/strong>. A series of comparative experiments was conducted to evaluate the impact of various parameters on training efficiency \u2013 such as speed limits, steering angle constraints, allowable deviation width from the lane center, movement continuity, discount factor, and frame rendering rate. Conclusion<\/strong>. The results of the study demonstrate the potential of reinforcement learning in the field of autonomous transport, while also highlighting the need for further training on real-world data, the prospects for scaling the approach to different classes of vehicles, and limitations related to computational resources and the need for safe behavior verification<\/p>\n\n\n\n

Keywords<\/strong>:<\/em><\/strong> reinforcement learning, unmanned vehicle, Q-learning, DQN (Deep Q-Network), actor-critic, simulation modeling, intelligent system, simulation environment, training stability<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Ushakov A.E., Stebulyanin M.M., Shereuzhev M.A., Devyatkin F.V. Development of an unmanned vehicle course control system based on reinforcement learning. News of the Kabardino-Balkarian Scientific Center of RAS.<\/em> 2025. Vol. 27. No. 3. Pp. 39\u201354. DOI: 10.35330\/1991-6639-2025-27-3-39-54<\/p>\n\n\n\n

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    Information about the authors<\/strong><\/summary>\n
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    Alexander E. Ushakov<\/strong>, Postgraduate student, Research Engineer, Department of Robotics and Mechatronics, Moscow State University of Technology \u201cSTANKIN\u201d;
    127055, Russia, Moscow, 1 Vadkovsky lane;
    ushakov_ae@internet.ru, ORCID: https:\/\/orcid.org\/0009-0006-1467-5043, SPIN-code: 5174-7378
    Mikhail M. Stebulyanin<\/strong>, Doctor of Technical Sciences, Professor, Head of the Department of Robotics and Mechatronics, Moscow State University of Technology \u201cSTANKIN\u201d;
    127055, Russia, Moscow, 1 Vadkovsky lane;
    mmsteb@rambler.ru, ORCID: https:\/\/orcid.org\/0009-0007-3443-0593, SPIN-code: 4389-1120
    Madin A. Shereuzhev<\/strong>, Candidate of Engineering Sciences, Associate Professor at the Department of Robotics and Mechatronics, Moscow State University of Technology \u201cSTANKIN\u201d;
    127055, Russia, Moscow, 1 Vadkovsky lane;
    shereuzhev@gmail.com, ORCID: https:\/\/orcid.org\/0000-0003-2352-992X, SPIN-code: 1734-9056
    Fedor V. Devyatkin<\/strong>, Postgraduate student at the Department of ME7 \u201cRobotic Systems and Mechatronics\u201d, The Bauman Moscow State Technical University;
    105005, Russia, Moscow, 5, 2-nd Baumanskaya street;
    Engineer, Moscow State University of Technology \u201cSTANKIN\u201d;
    127055, Russia, Moscow, 1 Vadkovsky lane;
    feodor-dev@ya.ru, ORCID: https:\/\/orcid.org\/0009-0000-2639-9521, SPIN-code: 7738-5724<\/p>\n\n\n\n

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    Development of an unmanned vehicle course control system based on reinforcement learning A.E. Ushakov, M.M. Stebulyanin, M.A. Shereuzhev, F.V. Devyatkin Upload the full text Abstract. At present, there is a growing development of autonomous transportation, driven by the need to improve road safety, reduce collisions, and enhance the efficiency of logistics operations. This trend is […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-3517","page","type-page","status-publish","hentry"],"yoast_head":"\n27.3.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-3-3-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.3.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Development of an unmanned vehicle course control system based on reinforcement learning A.E. Ushakov, M.M. Stebulyanin, M.A. Shereuzhev, F.V. Devyatkin Upload the full text Abstract. At present, there is a growing development of autonomous transportation, driven by the need to improve road safety, reduce collisions, and enhance the efficiency of logistics operations. 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