TBC-HRL: A Bio-Inspired Framework for Stable and Interpretable Hierarchical Reinforcement Learning
Hierarchical Reinforcement Learning (HRL) is effective for long-horizon and sparse-reward tasks by decomposing complex decision processes, but its real-world application remains limited due to instability between levels, inefficient subgoal scheduling, delayed responses, and poor interpretability. T...
Kaydedildi:
| Asıl Yazarlar: | , , |
|---|---|
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
MDPI AG
2025-10-01
|
| Seri Bilgileri: | Biomimetics |
| Konular: | |
| Online Erişim: | https://www.mdpi.com/2313-7673/10/11/715 |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|