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The function is useful together with plotHypergraph().

Usage

getHypergraph(mdp, ...)

Arguments

mdp

The MDP loaded using loadMDP().

...

Arguments passed to getInfo().

Value

A list representing the hypergraph with two elements: A tibble nodes and a tibble hyperarcs.

Examples

## Set working dir
wd <- setwd(system.file("models", package = "MDP2"))

#### A finite-horizon replacement problem ####
mdp<-loadMDP("machine1_")
#> Read binary files (0.000179105 sec.)
#> Build the HMDP (4.0801e-05 sec.)
#> Checking MDP and found no errors (2.4e-06 sec.)
plot(mdp)

plot(mdp, hyperarcColor = "label")  # colors based on labels

plot(mdp, hyperarcColor = "label", nodeLabel = "sId:label")  # node labels are 'sId: label'

plot(mdp, nodeLabel = "sIdx:label", radx = 0.02)  # adjust radx in nodes

scrapValues <- c(30, 10, 5, 0)  # scrap values (the values of the 4 states at stage 4)
runValueIte(mdp, "Net reward" , termValues = scrapValues)
#> Run value iteration with epsilon = 0 at most 1 time(s)
#> using quantity 'Net reward' under reward criterion.
#>  Finished. Cpu time 9.4e-06 sec.
plot(mdp, hyperarcColor = "policy")  # highlight optimal policy
#> Joining with `by = join_by(sId, aIdx)`

plot(mdp, hyperarcShow = "policy", nodeLabel = "weight")  # show only optimal policy



#### An infinite-horizon maintenance problem ####
mdp<-loadMDP("hct611-1_")
#> Read binary files (0.000122703 sec.)
#> Build the HMDP (2.54e-05 sec.)
#> Checking MDP and found no errors (1.6e-06 sec.)
plot(mdp)  # plot the first two stages

plot(mdp, hyperarcColor = "label")  # colors based on labels

plot(mdp, hyperarcColor = "label", nodeLabel = "sId:label")  # node labels are 'sId: label'

runPolicyIteAve(mdp,"Net reward","Duration")
#> Run policy iteration under average reward criterion using 
#> reward 'Net reward' over 'Duration'. Iterations (g): 
#> 1 (-0.512821) 2 (-0.446154) 3 (-0.43379) 4 (-0.43379) finished. Cpu time: 1.6e-06 sec.
#> [1] -0.43379
plot(mdp, hyperarcColor = "policy")  # highlight optimal policy
#> Joining with `by = join_by(sId, aIdx)`

plot(mdp, hyperarcShow = "policy")  # show only optimal policy



#### An infinite-horizon hierarchical replacement problem ####
library(magrittr)
mdp<-loadMDP("cow_")
#> Read binary files (0.000236306 sec.)
#> Build the HMDP (0.000189104 sec.)
#> Checking MDP and found no errors (4e-06 sec.)
hgf <- getHypergraph(mdp)
# modify labels
dat <- hgf$nodes %>% 
   dplyr::mutate(label = dplyr::case_when(
      label == "Low yield" ~ "L",
      label == "Avg yield" ~ "A",
      label == "High yield" ~ "H",
      label == "Dummy" ~ "D",
      label == "Bad genetic level" ~ "Bad",
      label == "Avg genetic level" ~ "Avg",
      label == "Good genetic level" ~ "Good",
      TRUE ~ "Error"
   ))
# assign nodes to grid ids
dat$gId[1:3]<-85:87
dat$gId[43:45]<-1:3
getGId<-function(process,stage,state) {
   if (process==0) start=18
   if (process==1) start=22
   if (process==2) start=26
   return(start + 14 * stage + state)
}
idx<-43
for (process in 0:2)
   for (stage in 0:4)
      for (state in 0:2) {
         if (stage==0 & state>0) break
         idx<-idx-1
         #cat(idx,process,stage,state,getGId(process,stage,state),"\n")
         dat$gId[idx]<-getGId(process,stage,state)
      }
hgf$nodes <- dat
# modify labels
dat <- hgf$hyperarcs %>% 
   dplyr::mutate(label = dplyr::case_when(
      label == "Replace" ~ "R",
      label == "Keep" ~ "K",
      label == "Dummy" ~ "D",
      TRUE ~ "Error"
   ),
   col = dplyr::case_when(
      label == "R" ~ "deepskyblue3",
      label == "K" ~ "darkorange1",
      label == "D" ~ "black",
      TRUE ~ "Error"
   ),
   lwd = 0.5,
   label = ""
   ) 
hgf$hyperarcs <- dat
# plot hypergraph
oldpar <- par(mai = c(0, 0, 0, 0))
plotHypergraph(gridDim = c(14, 7), hgf, cex = 0.8, radx = 0.02, rady = 0.03)

par(oldpar)

## Reset working dir
setwd(wd)