runPolicyIteDiscount: Perform policy iteration using the discounted expected-weight...

In MDP2: Markov Decision Processes (MDPs)

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

Description

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

Usage

runPolicyIteDiscount(
  mdp,
  w,
  dur,
  rate = 0,
  rateBase = 1,
  discountFactor = NULL,
  maxIte = 100,
  discountMethod = "continuous",
  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.
rate	The interest rate.
rateBase	The time-horizon the rate is valid over.
discountFactor	The discount rate for one time unit. If specified rate and rateBase are not used to calculate the discount rate.
maxIte	Max number of iterations. If the model does not satisfy the unichain assumption the algorithm may loop.
discountMethod	Either 'continuous' or 'discrete', corresponding to discount factor exp(-rate/rateBase) or 1/(1 + rate/rateBase), respectively. Only used if discountFactor is NULL.
objective	Optimize by maximizing ("max") or minimizing ("min") the Bellman value.
getLog	Output the log messages.

Value

Nothing.

See Also

getPolicy().

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