runPolicyIteAve: Perform policy iteration using the average expected-weight...

In MDP2: Markov Decision Processes (MDPs)

Perform policy iteration using the average expected-weight Bellman operator on the MDP.

Description

The policy can afterwards be received using functions getPolicy and getPolicyW.

Usage

runPolicyIteAve(
  mdp,
  w,
  dur,
  maxIte = 100,
  objective = c("max", "min"),
  getLog = TRUE
)

Arguments

mdp	The MDP loaded using loadMDP().
w	The label of the weight we optimize.
dur	The label of the duration/time such that discount rates can be calculated.
maxIte	Max number of iterations. If the model does not satisfy the unichain assumption the algorithm may loop.
objective	Optimize by maximizing ("max") or minimizing ("min") the Bellman value.
getLog	Output the log messages.

Value

The optimal gain (g) calculated.

See Also

getPolicy().

MDP2 documentation built on June 13, 2026, 1:08 a.m.