Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Scientific and Research Conference on Topical Issues in Aeronautics and Astronautics, TIAA 2016; Krasnoyarsk; Krasnoyarsk
Год издания: 2016
Идентификатор DOI: 10.1088/1757-899X/155/1/012001
Аннотация: This paper presents algorithm for generating neuroevolutionary multi-agent system that allows agents to learn from high-quality activities. Dissimilar traditional learning algorithms proposed algorithm combines student-teacher of-line learning and teaching agents based on sufficient activities producing by any agent in its subculture. The simulation studies demonstrated that the proposed algorithm is effective at rapidly generating near-optimal control agents.
Издание
Журнал: IOP Conference Series: Materials Science and Engineering
Выпуск журнала: 155
Номера страниц: 012001
Издатель: Institute of Physics Publishing
Персоны
- Engel E.A. (First Russian Doctoral Degree in Computer Sciences,Katanov State University of Khakassia)
- Engel N.E. (First Russian Doctoral Degree in Computer Sciences,Katanov State University of Khakassia)
- Kovalev I.V. (Second Russian Doctoral Degree in Computer Sciences,Siberian State Aerospace University)
Вхождение в базы данных
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