Q LEARNING REGRESSION NEURAL NETWORK


Creative Commons License

SARIGUL M. , AVCI M.

NEURAL NETWORK WORLD, cilt.28, ss.415-431, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 28 Konu: 5
  • Basım Tarihi: 2018
  • Doi Numarası: 10.14311/nnw.2018.28.023
  • Dergi Adı: NEURAL NETWORK WORLD
  • Sayfa Sayıları: ss.415-431

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.