Recursive Compositional Reinforcement Learning for Continuous Control Sürekli Kontrol Uygulamalari için Özyinelemeli Bileşimsel Pekiştirmeli Öǧrenme

Tanik G. O., Ertekin S.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 15 - 18 Mayıs 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu55565.2022.9864891
  • Basıldığı Şehir: Safranbolu
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: actor-critic, beta distribution, hierarchical learning, reinforcement learning
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.Compositional and temporal abstraction is the key to improving learning and planning in reinforcement learning. Modern real-world control problems call for continuous control domains and robust, sample efficient and explainable control frameworks. We are presenting a framework for recursively composing control skills to solve compositional and progressively complex tasks. The framework promotes reuse of skills, and as a result quickly adaptable to new tasks. The decision-tree can be observed, providing insight to the agents' behavior. Furthermore, the skills can be transferred, modified or trained independently, which can simplify reward shaping and increase training speeds considerably.