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dc.contributor.authorKabysh, Anton
dc.contributor.authorGolovko, Vladimir
dc.contributor.authorMikhniayeu, Andrei
dc.contributor.authorRubanau, Uladzimir
dc.contributor.authorLipnikas, Arunas
dc.coverage.spatialMinskru_RU
dc.date.accessioned2023-10-23T07:16:39Z
dc.date.available2023-10-23T07:16:39Z
dc.date.issued2011
dc.identifier.citationBehaviour patterns of adaptive multi-joined robot learned by multi-agent influence reinforcement learning / Anton Kabysh [et al.] // Pattern Recognition and Information Processing (PRIP'2011) : proceedings of the 11th International Conference, Minsk, 18–20 May 2011 / Belarusian State University of Informatics and Radioelectronics ; edition broard: Rauf Sadykhov [et al.]. – Minsk : BSUIR, 2011. – P. 392–396 : il. – Bibliogr.: p. 396 (20 titles).ru_RU
dc.identifier.urihttps://rep.bstu.by/handle/data/36653
dc.descriptionАнтон Кабыш, Владимир Головко, Андрей Михняев, Владимир Рубанов, Липницкая Арунас. Модели поведения адаптивного многосуставного робота, изученные с помощью мультиагентного обучения с подкреплением влиянияru_RU
dc.description.abstractThis paper describes behavior patterns produced by Multi-Joined Robot learned via Influence Reinforcement learning. This learning technique used for distributed, adaptive and self-organizing control in multi-agent system. This technique is quite simple and uses agent’s influences to estimate learning error between them. As will show, this learning rule supports positive-reward interactions between agents and does not require any additional information than standard reinforcement learning. The behavior patterns of learned robot shows that optimal behavior strategies differ for various learning techniques. As we will show, every algorithm produces his own behavior's patterns which are optimal for that learning rule to produce a faster convergence.ru_RU
dc.language.isoenru_RU
dc.publisherBSUIRru_RU
dc.subjectmulti-Agent Influence Reinforcementru_RU
dc.subjectусиление мультиагентного влиянияru_RU
dc.subjectlearningru_RU
dc.subjectобучениеru_RU
dc.subjectеligibility Tracesru_RU
dc.subjectследы соответствия требованиямru_RU
dc.subjectbehavior Patternsru_RU
dc.subjectмодели поведенияru_RU
dc.titleBehavior Patterns of adaptive Multi-Joined Robot learned by Multi-Agent Influence Reinforcement Learningru_RU
dc.title.alternativeМодели поведения адаптивного многосуставного робота, изученные с помощью мультиагентного обучения с подкреплением влиянияru_RU
dc.typeНаучный доклад (Working Paper)ru_RU


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