Nöral Adi Türevsel Denklemler ile Derin Pekiştirmeli Öğrenmeye Dayalı İç Mekan Görsel Navigasyonu

Year 2025, Volume: 27 Issue: 80, 224 - 233, 23.05.2025

Berk Ağın , Güleser Kalaycı Demir

https://doi.org/10.21205/deufmd.2025278008

Abstract

Son yıllarda, Derin Pekiştirmeli Öğrenme (DPÖ), mobil robot navigasyonun zorlu sorunlarını çözmek için umut vadeden bir yaklaşım olarak ortaya çıkmıştır. Bu çalışma, etkili eğitim ve bellek avantajı sunan Nöral Adi Türevsel Denklemler (NATD) kullanarak pekiştirmeli öğrenme yöntemini önermekte ve modelden bağımsız, noktadan-noktaya navigasyon için uygulamaktadır. NATD kullanımıyla, navigasyon performansında artış ve kaynak optimizasyonu ile adaptasyonda iyileştirme sağlanmıştır. Yaklaşımımızı doğrulamak için, gerçek dünya iç mekan sahneleri kullanılarak kapsamlı simulasyon çalışmaları yapıldı. Sonuçlar, önerdiğimiz NATD tabanlı metodolojinin, geleneksel ResNet ve CNN mimarilerine göre navigasyon performansını artırmada etkili olduğunu göstermiştir. Ayrıca, müfredat öğrenme stratejileri çalışmamıza entegre edilmiş ve ajanın aşamalı olarak daha karmaşık navigasyon senaryoları üzerinden öğrenmesi sağlanmıştır. Elde edilen sonuçlar, bu yaklaşım ile daha hızlı ve daha gürbüz pekiştirmeli öğrenmenin gerçeklenebildiğini göstermektedir.

Keywords

Nöral adi türevsel denklemler, resnet, derin pekiştirmeli öğrenme, görsel navigasyon, mobil robot

Indoor Visual Navigation Based on Deep Reinforcement Learning with Neural Ordinary Differential Equations

Abstract

Recently, Deep Reinforcement Learning (DRL) has gained attention as a promising approach to tackle the challenging problem of mobile robot navigation. This study proposes reinforcement learning utilizing Neural Ordinary Differential Equations (NODEs), which offer effective training and memory capacities, and applies it to model-free point-to-point navigation task. Through the use of NODEs, we achieved improvements in navigation performance as well as enhancements in resource optimization and adaptation. Extensive simulation studies were conducted using real-world indoor scenes to validate our approach. Results effectively demonstrated the effectiveness of our proposed NODEs-based methodology in enhancing navigation performance compared to traditional ResNet and CNN architectures. Furthermore, curriculum learning strategies were integrated into our study to enable the agent to learn through progressively more complex navigation scenarios. The results obtained indicate that this approach facilitates faster and more robust reinforcement learning.

Keywords

Neural ordinary differential equations, resnet, deep reinforcement learning, visual navigation, mobile robot

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