Control of technical object on the basis of the multi-agent system with neuroevolution and student-teacher of-line learning
[摘要] 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.
[发布日期] [发布机构] First Russian Doctoral Degree in Computer Sciences, Katanov State University of Khakassia, Abakan, Russia^1;Second Russian Doctoral Degree in Computer Sciences, Siberian State Aerospace University, Krasnoyarsk, Russia^2
[效力级别] [学科分类] 航空航天科学
[关键词] High quality;Learning and teachings;Near-optimal control;Neuro evolutions;Simulation studies;Student teachers;Technical objects;Traditional learning [时效性]
