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Function for building an HMDP model directly in memory.

Source: R/writer.R
memory_mdp_writer.Rd

memory_mdp_writer() defines the same main sub-functions as binary_mdp_writer(), but stores states and actions directly in C++ memory instead of writing intermediate binary files. close_writer() compiles the model and returns the loaded "HMDP" object.

Usage

memory_mdp_writer(
  prefix = "",
  eps = 1e-05,
  check = TRUE,
  verbose = FALSE,
  get_log = TRUE
)

Arguments

prefix

A character string kept for compatibility and stored in the returned object metadata.

eps

The sum of transition probabilities must at most differ eps from one when check = TRUE.

check

Check if the MDP seems correct before returning it.

verbose

More output when compiling and running algorithms.

get_log

Output the log messages.

Value

A list of functions. Calling close_writer() returns an "HMDP" object.

Details

External or included processes are not supported by memory_mdp_writer().

Note

Note all indexes are starting from zero (C/C++ style).

Examples

## Use temp dir
wd <- setwd(tempdir())

# Create a small HMDP with two levels
w<-memory_mdp_writer()
w$set_weights(c("Duration","Net reward","Items"))
w$process()
   w$stage()
      w$state(label="M0")
         w$action(label="A0",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
         w$action(label="A1",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(1,2,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                     w$action(label="A1",weights=c(0,10,5),prob=c(0,0,0.5,0,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
      w$end_state()
      w$state(label="M1")
         w$action(label="A0",weights=c(0,0,0),prob=c(2,0,1))
            w$process()
               w$stage()
                  w$state(label="D")
                     w$action(label="A0",weights=c(0,0,1),prob=c(1,0,0.5,1,1,0.5))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(0,0,0),prob=c(1,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
               w$stage()
                  w$state(label="C0")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
                  w$state(label="C1")
                     w$action(label="A0",weights=c(1,4,0),prob=c(0,0,1))
                     w$end_action()
                  w$end_state()
               w$end_stage()
            w$end_process()
         w$end_action()
      w$end_state()
   w$end_stage()
w$end_process()
w$close_writer()
#> 
#>   Statistics:
#>     states : 16 
#>     actions: 21 
#>     weights: 3 
#> 
#>   Closing memory MDP writer.
#> 
#> Build the HMDP from memory (9.8398e-05 sec.)
#> Checking MDP and found no errors (1.382e-06 sec.)
#> $bin_names
#> [1] "<memory>"
#> 
#> $time_horizon
#> [1] Inf
#> 
#> $states
#> [1] 16
#> 
#> $founder_states_last
#> [1] 2
#> 
#> $actions
#> [1] 21
#> 
#> $levels
#> [1] 2
#> 
#> $weight_names
#> [1] "Duration"   "Net reward" "Items"     
#> 
#> $weight_action_names
#> [1] "Duration"   "Net reward" "Items"     
#> 
#> $weight_trans_names
#> character(0)
#> 
#> $ptr
#> C++ object <0x5624be0498b0> of class 'HMDP' <0x5624bad18460>
#> 
#> attr(,"class")
#> [1] "HMDP" "list"

## Info about the binary files (don't have to load the model first)
if (FALSE) {
   get_bin_info_states()
   get_bin_info_actions()
}

## reset working dir
setwd(wd)