policyIteAve: Perform policy iteration (average criterion) on the MDP.
In MDP: Markov Decision Processes (MDPs) in R
Description
Usage
Arguments
Value
Author(s)
See Also
Description
The policy can afterwards be recieved using functions
getPolicy and getPolicyW.
Usage
1
policyIteAve(mdp, w, dur, maxIte = 100)
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.
Value
The optimal gain (g) calculated.
Author(s)
Lars Relund lars@relund.dk
See Also
MDP documentation built on May 2, 2019, 6:48 p.m.
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Description Usage Arguments Value Author(s) See Also
Description
The policy can afterwards be recieved using functions
getPolicy and getPolicyW.
Usage
1 | policyIteAve(mdp, w, dur, maxIte = 100)
|
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. |
Value
The optimal gain (g) calculated.
Author(s)
Lars Relund lars@relund.dk
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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