Sensör Tabanlı DDQN Modeline Ödül Fonksiyonu Belirleme

Yıl 2021, Sayı: 28, 539 - 544, 30.11.2021

Mehmet Gökçay Kabataş Sevinç İlhan Omurca

https://doi.org/10.31590/ejosat.1008702

Öz

Bu çalışmada DDQN Modeli ile Pekiştirmeli Öğrenme içerisinde 100 engeli/nesneyi geçmek üzere eğitilen ajanın uygun ödül fonksiyonunun belirlenmesi amaçlanmaktadır. Ajanı eğitmek için çevre alt problemlere bölümüştür. Alt problemler için çeşitli kurallar ve farklı ödül fonksiyonları tanımlanmıştır. Eğitim için gNet adında geliştirilmiş mini derin öğrenme kütüphanesi kullanılmıştır.

Anahtar Kelimeler

yapay zeka, derin öğrenme, DDQN, gNet, ödül fonksiyonu, sensor tabanlı

Setting Reward Function of Sensor Based DDQN Model

Öz

In this study, it is aimed to determine the appropriate reward function of the agent which trained to pass 100 obstacles/objects in Reinforcement Learning (RL) with Double Deep Q Network (DDQN) model. To train the agent, environment is split into sub problems. Several rules and different reward functions defined for the sub problems. A developed mini deep learning library which is called gNet is used for the training.

Anahtar Kelimeler

artificial intelligence, deep learning, DDQN, gNet, reward function, sensor based

Kaynakça

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