scholarly journals Exponential Convergence and Stability of Howard's Policy Improvement Algorithm for Controlled Diffusions

2020 ◽  
Vol 58 (3) ◽  
pp. 1314-1340
Author(s):  
Bekzhan Kerimkulov ◽  
David Šiška ◽  
Lukasz Szpruch
Keyword(s):  
Exponential Convergence ◽  
Policy Improvement ◽  
Convergence And Stability ◽  
Improvement Algorithm ◽  
Controlled Diffusions

scholarly journals On the policy improvement algorithm for ergodic risk-sensitive control

2020 ◽  
pp. 1-26
Author(s):  
Ari Arapostathis ◽  
Anup Biswas ◽  
Somnath Pradhan
Keyword(s):  
Control Problem ◽  
General Result ◽  
Region Of Attraction ◽  
Policy Improvement ◽  
Improvement Algorithm ◽  
Risk Sensitive ◽  
Risk Sensitive Control ◽  
Whole Space ◽  
Controlled Diffusions ◽  
Running Cost

In this article we consider the ergodic risk-sensitive control problem for a large class of multidimensional controlled diffusions on the whole space. We study the minimization and maximization problems under either a blanket stability hypothesis, or a near-monotone assumption on the running cost. We establish the convergence of the policy improvement algorithm for these models. We also present a more general result concerning the region of attraction of the equilibrium of the algorithm.

A Class of Decision Processes Showing Policy-Improvement/Newton–Raphson Equivalence

1989 ◽  
Vol 3 (3) ◽  
pp. 397-403 ◽  
Author(s):  
P. Whittle
Keyword(s):  
Optimality Condition ◽  
Regularity Condition ◽  
Decision Processes ◽  
Policy Improvement ◽  
Improvement Algorithm ◽  
Newton Raphson ◽  
Raphson Algorithm

A condition expressed in Eq. (7) is given which, with one simplifying regularity condition, ensures that the policy-improvement algorithm is equivalent to application of the Newton–Raphson algorithm to an optimality condition. It is shown that this condition covers the two known cases of such equivalence, and another example is noted. The condition is believed to be necessary to within transformations of the problem, but this has not been proved.

A Policy Improvement Algorithm for Solving a Mixture Class of Perfect Information and AR-AT Semi-Markov Games

2020 ◽  
Vol 22 (02) ◽  
pp. 2040008
Author(s):  
P. Mondal ◽  
S. K. Neogy ◽  
A. Gupta ◽  
D. Ghorui
Keyword(s):  
Perfect Information ◽  
Policy Improvement ◽  
Markov Games ◽  
Markov Game ◽  
Improvement Algorithm ◽  
Finite State ◽  
Markov Decision ◽  
Mixture Class ◽  
Zero Sum ◽  
Action Spaces

Zero-sum two-person discounted semi-Markov games with finite state and action spaces are studied where a collection of states having Perfect Information (PI) property is mixed with another collection of states having Additive Reward–Additive Transition and Action Independent Transition Time (AR-AT-AITT) property. For such a PI/AR-AT-AITT mixture class of games, we prove the existence of an optimal pure stationary strategy for each player. We develop a policy improvement algorithm for solving discounted semi-Markov decision processes (one player version of semi-Markov games) and using it we obtain a policy-improvement type algorithm for computing an optimal strategy pair of a PI/AR-AT-AITT mixture semi-Markov game. Finally, we extend our results when the states having PI property are replaced by a subclass of Switching Control (SC) states.

scholarly journals On the policy improvement algorithm in continuous time

Stochastics ◽  
2016 ◽  
Vol 89 (1) ◽  
pp. 348-359 ◽  
Author(s):  
Saul D. Jacka ◽  
Aleksandar Mijatović
Keyword(s):  
Continuous Time ◽  
Policy Improvement ◽  
Improvement Algorithm
Sign in / Sign up

Export Citation Format

Share Document