Perform policy iteration (average reward criterion) on the MDP.

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

Usage

runPolicyIteAve(mdp, w, dur, maxIte = 100, 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.

getLog

Output the log messages.

Value

The optimal gain (g) calculated.

See also

getPolicy().