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- Дата: 25-04-2021, 16:41
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Автор: Changsheng Hua
Издательство: Springer
Год: 2021
Страниц: 139
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic (NAC) methods, respectively. Their effectiveness has been demonstrated by an experimental study on a brushless direct current motor test rig. Reinforcement learning (RL) is a branch of Machine Learning (ML) concerned with how agents take actions by interacting with their environment to maximize some notion of cumulative reward. It is a powerful tool to solve optimization problems in a data-driven manner. There have been a lot of remarkable results over the last few decades about applications of RL to performance optimization such as playing Atari games and Go with super-human performance, and training robotics to learn primitive skills.