Q LEARNING REGRESSION NEURAL NETWORK
NEURAL NETWORK WORLD, cilt.28, sa.5, ss.415-431, 2018 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 28 Sayı: 5
- Basım Tarihi: 2018
- Doi Numarası: 10.14311/nnw.2018.28.023
- Dergi Adı: NEURAL NETWORK WORLD
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.415-431
- Anahtar Kelimeler: reinforcement learning, q learning, q value function approximation, general regression neural network, kernel based regression, REINFORCEMENT, TEMPERATURE, MODEL
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Çukurova Üniversitesi Adresli: Evet
Özet
In this work, a Nadaraya-Watson kernel based learning system which owns general regression neural network topology is adapted to Q learning method to evaluate a quick and efficient action selection policy for reinforcement learning problems. By means of the proposed method Q value function is generalized and learning speed of Q agent is accelerated. The training data of the developed neural network are obtained by a standard Q learning agent on closed-loop simulation system. The efficiency of the proposed method is tested on popular reinforcement learning benchmarks and its performance is compared with other popular regression methods and Q-learning utilized methods. QLRNN increased the learning performance and it learns faster than other methods on selected benchmarks. Test results showed the efficiency and the importance of the proposed network.