On-Policy Deep Reinforcement Learning Approach to Multi Agent Problems
Yazarlar (2)
Fırat Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | Interdisciplinary Research in Technology and Management(IRTM) |
| Kongre Tarihi | 26-02-2021 / |
| Basıldığı Ülke | Hindistan |
| Basıldığı Şehir | Kolkata |
| Bildiri Linki | https://www.taylorfrancis.com/chapters/edit/10.1201/9781003202240-58/policy-deep-reinforcement-learning-approach-multi-agent-problems-ziya-tan-mehmet-karakose |
| Özet |
| Reinforcement learning approach has been preferred by researchers and scientists in recent years, especially due to its superior performance in robot studies. While smart systems are becoming widespread in technology that develops and changes day by day, communication problems between these systems and the environment are still among the issues that are being studied with great importance. Reinforcement learning determines what action to take in the next step by rewarding the experiences gained from the environment it is in. The most important difference from other machine learning approaches is that it does not need any preliminary data during the training phase. In this study, a deep reinforcement learning method that regulates the movements of three different robots used in limited areas is presented. The performance of the robots has been tested by training this problem with the Policy … |
| Anahtar Kelimeler |
