Optimized Deep Reinforcement Learning Approach for Dynamic System
Yazarlar (2)
Dr. Öğr. Üyesi Ziya TAN
Erzincan Binali Yıldırım Üniversitesi
Fırat Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Web of Science Kapsamındaki Kongre/Sempozyum |
| DOI Numarası | 10.1109/isse49799.2020.9272245 |
| Bildiri Dili | İngilizce |
| Kongre Adı | 6th IEEE International Symposium on Systems Engineering (ISSE2020) |
| Kongre Tarihi | 12-10-2020 / 14-10-2020 |
| Basıldığı Ülke | |
| Basıldığı Şehir | |
| Bildiri Linki | https://ieeexplore.ieee.org/document/9272245 |
| Özet |
| Reinforcement learning methods provide significant and impressive improvements in artificial intelligence studies in recent years, especially in Atari and Go games. This development attracts the attention of scientists who try to understand how people learn. In addition, the biggest advantage of reinforcement learning over other learning algorithms is that it does not require any prior data. This feature distinguishes Reinforcement Learning from others. Reinforcement Learning is an approach in which smart programs work in a certain or uncertain environment to constantly adapt and learn based on scoring. The feedback process is also known as a reward or called a penalty. Given Agents and environment, it is determined which action to take. In this study, we apply the success of Deep Q-Learning (DQL) algorithm, one of the model-free based deep reinforcement learning algorithms used in the literature, on the … |
| Anahtar Kelimeler |
| CartPole | Deep Reinforcement Learning | Deep Q-Learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 10 |
| Google Scholar | 16 |