New value iteration and Q-learning methods for the average cost dynamic programming problem

2002 ◽  
Author(s):  
D.P. Bertsekas
Keyword(s):  
Dynamic Programming ◽  
Programming Problem ◽  
Average Cost ◽  
Value Iteration ◽  
Learning Methods ◽  
Q Learning ◽  
Dynamic Programming Problem

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.

scholarly journals A Stochastic Dynamic Programming Approach Based on Bounded Rationality and Application to Dynamic Portfolio Choice

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wenjie Bi ◽  
Liuqing Tian ◽  
Haiying Liu ◽  
Xiaohong Chen
Keyword(s):  
Dynamic Programming ◽  
Interest Rate ◽  
Bounded Rationality ◽  
Programming Problem ◽  
Stochastic Volatility ◽  
Portfolio Choice ◽  
Programming Approach ◽  
Dynamic Programming Problem ◽  
Dynamic Portfolio ◽  
Dynamic Portfolio Choice

Dynamic portfolio choice is an important problem in finance, but the optimal strategy analysis is difficult when considering multiple stochastic volatility variables such as the stock price, interest rate, and income. Besides, recent research in experimental economics indicates that the agent shows limited attention, considering only the variables with high fluctuations but ignoring those with small ones. By extending the sparse max method, we propose an approach to solve dynamic programming problem with small stochastic volatility and the agent’s bounded rationality. This approach considers the agent’s behavioral factors and avoids effectively the “Curse of Dimensionality” in a dynamic programming problem with more than a few state variables. We then apply it to Merton dynamic portfolio choice model with stochastic volatility and get a tractable solution. Finally, the numerical analysis shows that the bounded rational agent may pay no attention to the varying equity premium and interest rate with small variance.

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