Araştırma Makalesi

Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm

Cilt: 6 Sayı: 2 23 Eylül 2023
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Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm

Öz

Machine learning methods can generally be categorized as supervised, unsupervised and reinforcement learning. One of these methods, Q learning algorithm in reinforcement learning, is an algorithm that can interact with the environment and learn from the environment and produce actions accordingly. In this study, eight different on-line methods have been proposed to determine online the value of the learning parameter in the Q learning algorithm depending on different situations. In order to test the performance of the proposed methods, these algorithms are applied to Frozen Lake and Car Pole systems and the results are compared graphically and statistically. When the obtained results are examined, Method 1 has produced better performance for Frozen Lake, which is a discrete system, while Method 7 has produced better results for the Cart Pole System, which is a continuous system.

Anahtar Kelimeler

Kaynakça

  1. Adigüzel, F., Yalçin, Y., 2018. Discrete-Time Backstepping Control for Cart-Pendulum System with Disturbance Attenuation via I&i Disturbance Estimation. in 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).
  2. Adıgüzel, F., Yalçin, Y., 2022. “Backstepping Control for a Class of Underactuated Nonlinear Mechanical Systems with a Novel Coordinate Transformation in the Discrete-Time Setting.” in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering.
  3. Akyurek, H.A., Bucak İ.Ö., 2012. Zamansal-Fark, Uyarlanır Dinamik Programlama ve SARSA Etmenlerinin Tipik Arazi Aracı Problemi Için Öğrenme Performansları. in Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu. Trabzon.
  4. Angiuli, A., Fouque J.P., Laurière M., 2022. Unified Reinforcement Q-Learning for Mean Field Game and Control Problems. Mathematics of Control, Signals, and Systems 34(2):217–71.
  5. Barlow, H. B., 1989. Unsupervised Learning. Neural Computation 1(3).
  6. Barto, A. G., Sutton R.S., Anderson C.W., 1983. Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems. IEEE Transactions on Systems, Man, and Cybernetics 5(834–846).
  7. Bayraj, E. A., Kırcı, P., Ensari, T., Seven, E., Dağtekin, M., 2022. Göğüs Kanseri Verileri Üzerinde Makine Öğrenmesi Yöntemlerinin Uygulanması. Journal of Intelligent Systems: Theory and Applications 5(1):35–41.
  8. Bucak, I.Ö., Zohdy M. A., 1999. Application Of Reinforcement Learning Control To A Nonlinear Bouncing Cart. Pp. 1198–1202 in Proceedings of the American Control Conference. San Diego, California.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka, Elektrik Mühendisliği

Bölüm

Araştırma Makalesi

Erken Görünüm Tarihi

23 Eylül 2023

Yayımlanma Tarihi

23 Eylül 2023

Gönderilme Tarihi

3 Mart 2023

Kabul Tarihi

6 Eylül 2023

Yayımlandığı Sayı

Yıl 2023 Cilt: 6 Sayı: 2

Kaynak Göster

APA
Çimen, M. E., Garip, Z., Yalçın, Y., Kutlu, M., & Boz, A. F. (2023). Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm. Journal of Intelligent Systems: Theory and Applications, 6(2), 191-198. https://doi.org/10.38016/jista.1250782
AMA
1.Çimen ME, Garip Z, Yalçın Y, Kutlu M, Boz AF. Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm. jista. 2023;6(2):191-198. doi:10.38016/jista.1250782
Chicago
Çimen, Murat Erhan, Zeynep Garip, Yaprak Yalçın, Mustafa Kutlu, ve Ali Fuat Boz. 2023. “Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm”. Journal of Intelligent Systems: Theory and Applications 6 (2): 191-98. https://doi.org/10.38016/jista.1250782.
EndNote
Çimen ME, Garip Z, Yalçın Y, Kutlu M, Boz AF (01 Eylül 2023) Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm. Journal of Intelligent Systems: Theory and Applications 6 2 191–198.
IEEE
[1]M. E. Çimen, Z. Garip, Y. Yalçın, M. Kutlu, ve A. F. Boz, “Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm”, jista, c. 6, sy 2, ss. 191–198, Eyl. 2023, doi: 10.38016/jista.1250782.
ISNAD
Çimen, Murat Erhan - Garip, Zeynep - Yalçın, Yaprak - Kutlu, Mustafa - Boz, Ali Fuat. “Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm”. Journal of Intelligent Systems: Theory and Applications 6/2 (01 Eylül 2023): 191-198. https://doi.org/10.38016/jista.1250782.
JAMA
1.Çimen ME, Garip Z, Yalçın Y, Kutlu M, Boz AF. Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm. jista. 2023;6:191–198.
MLA
Çimen, Murat Erhan, vd. “Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm”. Journal of Intelligent Systems: Theory and Applications, c. 6, sy 2, Eylül 2023, ss. 191-8, doi:10.38016/jista.1250782.
Vancouver
1.Murat Erhan Çimen, Zeynep Garip, Yaprak Yalçın, Mustafa Kutlu, Ali Fuat Boz. Self Adaptive Methods for Learning Rate Parameter of Q-Learning Algorithm. jista. 01 Eylül 2023;6(2):191-8. doi:10.38016/jista.1250782

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