Reinforcement Learning for Routing Strategy Considering the State of Network Link

2012 ◽  
Vol 220-223 ◽  
pp. 2772-2776
Author(s):  
Zhao Hui Hu
Keyword(s):  
Dynamic Programming ◽  
Reinforcement Learning ◽  
Programming Problem ◽  
Efficient Algorithm ◽  
The State ◽  
Routing Strategy ◽  
Network Link ◽  
Dynamic Programming Problem

This paper presents reinforcement learning (RL) algorithm for routing strategy considering the state of network link, which can be deemed as a dynamic programming problem with stochastic needs. Through modeling those four elements and experiments, we draw the conclusion that upon the state of network link, RL is an efficient algorithm for routing strategy; the data can be efficient forwarded to the destination.

THE CALCULATION OF THE LIFETIME OF THE EQUIPMENT WITH APPLICATION OF DYNAMIC PROGRAMMING

2020 ◽  
Author(s):  
Sergey Ivanovich Makarov ◽  
◽  
Maria Vladimirovna Kurganova ◽  
Keyword(s):  
Dynamic Programming ◽  
Economic Analysis ◽  
Programming Problem ◽  
Calculation Methods ◽  
Optimal Operating ◽  
Operating Period ◽  
Equipment Replacement ◽  
Dynamic Programming Problem ◽  
Replacement Time

The dynamic programming problem for finding the optimal operating period and equipment replacement time using a combination of graphical and calculation methods of economic analysis is considered.

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