Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels have high accuracy. It is important to notice that currently in autonomous driving systems for many tasks classical methods of supervised learning are no longer applicable. In this paper we are interested in a specific problem, that is to control a car to move along a given reference trajectory using reinforcement learning algorithms. In control theory, this problem is called an optimal control problem for moving along the reference trajectory. Airsim environment is used to simulate a moving car for a fixed period of time without obstacles. The purpose of our research is to determine the best reinforcement learning algorithm for a formulated problem among state-of-the-art algorithms such as DDPG, PPO, SAC, DQN and others. As a result of the conducted training and testing, it was revealed that the best algorithm for this problem is A2C.
Язык оригинала | английский |
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Название основной публикации | Mathematical Optimization Theory and Operations Research |
Подзаголовок основной публикации | Recent Trends - 21st International Conference, MOTOR 2022, Revised Selected Papers |
Редакторы | Yury Kochetov, Anton Eremeev, Oleg Khamisov, Anna Rettieva |
Издатель | Springer Nature |
Страницы | 338-349 |
Число страниц | 12 |
ISBN (печатное издание) | 9783031162237 |
DOI | |
Состояние | Опубликовано - 2022 |
Событие | 21st International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2022 - Petrozavodsk, Российская Федерация Продолжительность: 2 июл 2022 → 6 июл 2022 |
Название | Communications in Computer and Information Science |
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Том | 1661 CCIS |
ISSN (печатное издание) | 1865-0929 |
ISSN (электронное издание) | 1865-0937 |
конференция | 21st International Conference on Mathematical Optimization Theory and Operations Research , MOTOR 2022 |
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Страна/Tерритория | Российская Федерация |
Город | Petrozavodsk |
Период | 2/07/22 → 6/07/22 |
ID: 101415278