Proximal Policy Based Deep Reinforcement Learning Approach for Swarm Robots
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 |
| DOI Numarası | 10.1109/ZINC52049.2021.9499288 |
| Bildiri Dili | İngilizce |
| Kongre Adı | 2021 Zooming Innovation in Consumer Technologies Conference (ZINC) |
| Kongre Tarihi | 04-01-2021 / |
| Basıldığı Ülke | Sırbistan |
| Basıldığı Şehir | Novi-Sad |
| Bildiri Linki | https://ieeexplore.ieee.org/abstract/document/9499288/ |
| Özet |
| Artificial intelligence technology is becoming more active in all areas of our lives day by day. This technology affects our daily life by more developing in areas such as industry 4.0, security and education. Deep reinforcement learning is one of the most developed algorithms in the field of artificial intelligence. In this study, it is aimed that three different robots in a limited area learn to move without hitting each other, fixed obstacles and the boundaries of the field. These robots have been trained using the deep reinforcement learning approach and Proximal policy optimization (PPO) policy. Instead of uses value-based methods with the discrete action space, PPO that can easily manipulate the continuous action field and successfully determine the action of the robots has been proposed. PPO policy achieves successful results in multi-agent problems, especially with the use of the Actor-Critic network. In addition … |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 14 |