ARTÍCULO
TITULO

Global Reinforcement Learning in Neural Networks

Ma    
X.    
Likharev    
K.K.    

Resumen

No disponible

 Artículos similares

       
 
Yonggai Dai, Zongchen Li and Boyu Wang    
Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The curre... ver más

 
Yihan Niu, Feixiang Zhu, Moxuan Wei, Yifan Du and Pengyu Zhai    
Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The... ver más

 
Wongwan Jung and Daejun Chang    
This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane ... ver más

 
Francesco Nebula, Roberto Palumbo, Gabriella Gigante and Angela Vozella    
Nowadays, in view of the growing traffic volume, an appropriate aircraft sequencing in the arrival sector is needed to maintain safety levels and improve the performance of the runway system and flight times. This paper presents a digital assistant suppo... ver más
Revista: Information

 
Haojie Zhu, Mou Chen, Zengliang Han and Mihai Lungu    
This paper concerns the fire-control command calculation (FCCC) of an unmanned autonomous helicopter (UAH). It determines the final effect of the UAH attack. Although many different FCCC methods have been proposed for finding optimal or near-optimal fire... ver más
Revista: Aerospace