{"id":2459,"date":"2025-06-22T13:35:53","date_gmt":"2025-06-22T12:35:53","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2459"},"modified":"2026-04-13T10:39:05","modified_gmt":"2026-04-13T09:39:05","slug":"27-2-6","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/27-2-6\/","title":{"rendered":"27.2.6"},"content":{"rendered":"\n

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\u041a\u043b\u044e\u0447\u0435\u0432\u044b\u0435 \u0441\u043b\u043e\u0432\u0430:<\/strong> <\/em>\u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441 \u043f\u043e\u0434\u043a\u0440\u0435\u043f\u043b\u0435\u043d\u0438\u0435\u043c, \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u0430\u0433\u0435\u043d\u0442\u044b, \u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u0430\u044f \u0442\u0440\u0430\u0435\u043a\u0442\u043e\u0440\u0438\u044f, \u0432\u044b\u0441\u043e\u043a\u043e\u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0435 \u0442\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442\u043d\u044b\u0435 \u0441\u0440\u0435\u0434\u0441\u0442\u0432\u0430, \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043f\u043e\u043b\u0438\u0442\u0438\u043a, \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b \u00ab\u0430\u043a\u0442\u0435\u0440-\u043a\u0440\u0438\u0442\u0438\u043a\u00bb, \u0438\u043c\u0438\u0442\u0430\u0446\u0438\u043e\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435, \u0441\u0435\u043d\u0441\u043e\u0440\u044b, \u043d\u0435\u043f\u0440\u0435\u0440\u044b\u0432\u043d\u044b\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f, \u0434\u0438\u0441\u043a\u0440\u0435\u0442\u043d\u044b\u0435 \u0441\u043e\u0441\u0442\u043e\u044f\u043d\u0438\u044f, PPO, SAC<\/p>\n\n\n\n

\u0414\u043b\u044f \u0446\u0438\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f.<\/strong> \u0413\u043e\u0440\u043e\u0434\u043d\u0438\u0447\u0435\u0432 \u041c. \u0413. \u041e \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0441 \u043f\u043e\u0434\u043a\u0440\u0435\u043f\u043b\u0435\u043d\u0438\u0435\u043c \u0432 \u0437\u0430\u0434\u0430\u0447\u0435 \u0432\u044b\u0431\u043e\u0440\u0430 \u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0439 \u0442\u0440\u0430\u0435\u043a\u0442\u043e\u0440\u0438\u0438 \u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \/\/ \u0418\u0437\u0432\u0435\u0441\u0442\u0438\u044f \u041a\u0430\u0431\u0430\u0440\u0434\u0438\u043d\u043e-\u0411\u0430\u043b\u043a\u0430\u0440\u0441\u043a\u043e\u0433\u043e \u043d\u0430\u0443\u0447\u043d\u043e\u0433\u043e \u0446\u0435\u043d\u0442\u0440\u0430 \u0420\u0410\u041d. 2025. \u0422. 27. \u2116 2. \u0421. 86\u2013102.  DOI: 10.35330\/1991-6639-2025-27-2-86-102<\/p>\n\n\n\n

\u0421\u043f\u0438\u0441\u043e\u043a \u043b\u0438\u0442\u0435\u0440\u0430\u0442\u0443\u0440\u044b<\/summary>\n
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  14. Lillicrap T.P., Hunt J.J., Pritzel A. et al. Continuous control with deep reinforcement learning. arXiv:1509.02971v1. 2015. file:\/\/\/C:\/Users\/%D0%90%D1%80%D1%81%D0%B5%D0%BD\/ Downloads\/1509.02971v1.pdf<\/li>\n\n\n\n
  15. Chen Y., Lam C.T., Pau G., Ke W. From Virtual to Reality: A Deep Reinforcement Learning Solution to Implement Autonomous Driving with 3D-LiDAR. Applied Sciences.<\/em>2025. Vol. 15. No. 3. P. 1423. DOI: 10.3390\/app15031423<\/li>\n\n\n\n
  16. Guoyu Zuo, Kexin Chen, Jiahao Lu, Xiangsheng Huang. Deterministic generative adversarial imitation learning. Neurocomputing.<\/em> 2020. Vol. 388. Pp. 60\u201369. ISSN 0925-2312.  DOI: 10.1016\/j.neucom.2020.01.016<\/li>\n\n\n\n
  17. Sawada  R. Automatic Collision Avoidance Using Deep Reinforcement Learning with Grid Sensor. In: Sato, H., Iwanaga, S., Ishii, A. (eds). Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems.<\/em> IES 2019. Proceedings in Adaptation, Learning and Optimization. Springer, Cham.  2020. Vol. 12. Pp. 17\u201332. DOI: 10.1007\/978-3-030-37442-6_3<\/li>\n\n\n\n
  18. Hachaj T., Piekarczyk M. On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data. Applied Sciences.<\/em> <\/strong>2025. Vol. 15. No. 2. P. 538. DOI: 10.3390\/app15020538<\/li>\n<\/ol>\n<\/details>\n\n\n\n
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    \n

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    111024, \u0420\u043e\u0441\u0441\u0438\u044f, \u041c\u043e\u0441\u043a\u0432\u0430, \u0443\u043b. \u0410\u0432\u0438\u0430\u043c\u043e\u0442\u043e\u0440\u043d\u0430\u044f, 8\u0410;m.g.gorodnichev@mtuci.ru, ORCID: https:\/\/orcid.org\/0000-0003-1739-9831, <\/strong>SPIN-\u043a\u043e\u0434: <\/strong>4576-9642<\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

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