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Optimizing Reinforcement Learning Control Model in Furuta Pendulum and Transferring it to Real-World
Autor:
Myung Rae Hong, Sanghun Kang, Jingoo Lee, Sungchul Seo, Seungyong Han, Je-Sung Koh, Daeshik Kang
Publikováno v:
IEEE Access, Vol 11, Pp 95195-95200 (2023)
Reinforcement learning does not require explicit robot modeling as it learns on its own based on data, but it has temporal and spatial constraints when transferred to real-world environments. In this research, we trained a balancing Furuta pendulum p
Externí odkaz:
https://doaj.org/article/c245d9e28a4048cbac96e5eb5b2bdd9f
