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Dergiler / Sakarya University Journal of Science / 2013 / Cilt: 17 Sayı: 3

Yarı Markov Karar Süreci Problemlerinin Çözümünde Çok Katmanlı Yapay Sinir Ağlarıyla Fonksiyon Yaklaşımlı Ödüllü Öğrenme Algoritması

A Reinforcement Learning Algorithm Using Multi-Layer Artificial Neural Networks for Semi-Markov Decision Problems

Mustafa Ahmet Beyazıt OCAKTAN
Ufuk KULA
Sayfa
307–307
Yayın Tarihi
2013-06-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to results of numerical examples, a number of hidden layer are the key success factors, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Real life problems are generally large-scale and difficult to model.
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Temel Bulgular & Veri:Performance of the developed algorithm is measured and compared to the classical reinforcement algorithm on a small-scale numerical example.
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Sonuç & Katkı:According to results of numerical examples, a number of hidden layer are the key success factors, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Markov/Yarı Markov Karar Süreci · Ödüllü Öğrenme · Çok Katmanlı Yapay Sinir Ağları

Özet

Real life problems are generally large-scale and difficult to model. Therefore, these problems can't be mostly solved by classical optimisation methods. This paper presents a reinforcement learning algorithm using a multi-layer artificial neural network to find an approximate solution for large-scale semi Markov decision problems. Performance of the developed algorithm is measured and compared to the classical reinforcement algorithm on a small-scale numerical example. According to results of numerical examples, a number of hidden layer are the key success factors, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.

Abstract

Real life problems are generally large-scale and difficult to model. Therefore, these problems can’t be mostly solved by classical optimization methods. This paper presents a reinforcement learning algorithm using a multi-layer artificial neural network to find an approximate solution for large-scale semi Markov decision problems. Performance of the developed algorithm is measured and compared to the classical reinforcement algorithm on a small-scale numerical example. According to results of numerical examples, the number of hidden layer is the key success factor, and average cost of the solution generated by the developed algorithm is approximately equal to that generated by the classical reinforcement algorithm.

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Bu Makaleye Atıf Yap

OCAKTAN, M. A. B., KULA, U. (2013). Yarı Markov Karar Süreci Problemlerinin Çözümünde Çok Katmanlı Yapay Sinir Ağlarıyla Fonksiyon Yaklaşımlı Ödüllü Öğrenme Algoritması. Sakarya University Journal of Science, Cilt: 17 Sayı: 3, 307–307. https://ajindex.com/articles/942461

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Kaynakça (11)

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