Optimizing Reinforcement Learning Control Model in Furuta Pendulum and Transferring it to Real-World
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 problem, which is difficult to model, in a virtual...
Kaydedildi:
| Asıl Yazarlar: | , , , , , , |
|---|---|
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2023-01-01
|
| Seri Bilgileri: | IEEE Access |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/10234431/ |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|