Experimental evaluation of multi-agent reinforcement learning in real-world scale-free networks
Multi-agent reinforcement learning is a common method for optimizing agents' local decision in a distributed and scalable manner. However, the study and analysis of the state-of-the-art multi-agent reinforcement learning (MARL) algorithms have been limited to small problems involving few number...
محفوظ في:
| المؤلف الرئيسي: | Al Hashimi, Rashid (author) |
|---|---|
| منشور في: |
2010
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://bspace.buid.ac.ae/handle/1234/42 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Studying cooperative multi-agent reinforcement learning in networks
حسب: Oudah, Mayada Mohamed
منشور في: (2012) -
Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
حسب: Alhusin, Mohammed Omer Alamin
منشور في: (2019) -
Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems
حسب: Emna Baccour (16896366)
منشور في: (2024) -
Multi-Objective Task Allocation Via Multi-Agent Coalition Formation
حسب: Amer, Noha Tarek
منشور في: (2012) -
Experimental investigation and multi-scale Mori–Tanaka modeling of viscoelastic asphalt mastic with imperfect interfaces
حسب: K. Lakshmi Roja (14159028)
منشور في: (2025)