Q-Learning Algoritmasının Öğrenme Hızı Parametresi için Kendine Uyarlamalı Yöntemler parametresi

Year 2023, Volume: 6 Issue: 2, 191 - 198, 23.09.2023

Murat Erhan Çimen Zeynep Garip Yaprak Yalçın Mustafa Kutlu Ali Fuat Boz

https://doi.org/10.38016/jista.1250782

Abstract

Makine öğrenmesi yöntemleri genel olarak denetimli, denetimsiz ve takviyeli öğrenme olarak sınıflandırılabilir. Bu yöntemlerden biri olan takviyeli öğrenme içerisinde bulunan Q learning algoritması ortamla etkileşime girerek ortamdan öğrenebilen ve ona göre aksiyonlar üretebilen bir algoritmadır. Bu çalışmada Q learning algoritması içerisinde bulunan öğrenme parametresinin değeri için 8 farklı yöntem önerilmiştir. Önerilen yöntemlerin performanslarının test edilebilmesi için donmuş göl ve ters sarkaç sistemlerine bu algoritmalar uygulanmış ve sonuçları grafiksel ve istatistiksel olarak karşılaştırılmıştır. Elde edilen sonuçlar incelendiğinde ayrık bir sistem olan Donmuş Göl sistemi için Metot 1 daha iyi performans sergilerken sürekli bir sistem olan Ters Sarkaç Sistemi için Metot 7 daha iyi sonuç göstermiştir.

Keywords

Takviyeli Öğrenme, Q Learning, Makine Öğrenmesi

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

Abstract

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.

Keywords

Reinforcement Learning, Q learning, Machine Learning

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