A Comparison of Policy Iteration Methods for Solving Continuous-State, Infinite-Horizon Markovian Decision Problems Using Random, Quasi-random, and Deterministic Discretizations

1997 ◽  
Author(s):  
John P. Rust
Keyword(s):  
Infinite Horizon ◽  
Policy Iteration ◽  
Decision Problems ◽  
Continuous State ◽  
Iteration Methods ◽  
Markovian Decision Problems

Bounds and good policies in stationary finite–stage Markovian decision problems

1980 ◽  
Vol 12 (1) ◽  
pp. 154-173 ◽  
Author(s):  
Gerhard Hübner
Keyword(s):  
Decision Model ◽  
Transition Probabilities ◽  
Planning Horizon ◽  
Decision Problems ◽  
Optimal Decisions ◽  
Optimal Value ◽  
Action Spaces ◽  
Stationary Problems ◽  
Markovian Decision Problems

A stationary Markovian decision model is considered with general state and action spaces where the transition probabilities are weakened to be bounded transition measures (this is useful for many applications). New and improved bounds are given for the optimal value of stationary problems with a large planning horizon if either only a few steps of iteration are carried out or, in addition, a solution of the infinite-stage problem is known. Similar estimates are obtained for the quality of policies which are composed of nearly optimal decisions from the first few steps or from the infinite-stage solution.

Truncated policy iteration methods

1984 ◽  
Vol 3 (5) ◽  
pp. 243-246 ◽  
Author(s):  
Ron S. Dembo ◽  
Moshe Haviv
Keyword(s):  
Policy Iteration ◽  
Iteration Methods
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