tab_actions: Compute policy if species not seen

In conservation-decisions/smsPOMDP: POMDP problem solver: When to stop managing or surveying cryptic threatened species ?

Description

Computes the best policy to follow if the species is not seen. Summarized in a data.frame

Usage

tab_actions(transition, observation, reward, state_prior, disc = 0.95, Tmax = 100)

Arguments

transition	Transition matrix between states, can be computed using tr
observation	observation matrix for each action, can be computed using obs
reward	reward matrix, can be computed using rew
state_prior	Initial belief state, vector of 2 values (belief state extant and extinct), between 0 and 1.
disc	Discount factor used to compute the policy (default 0.95)
Tmax	Maximal horizon time step

Value

data.frame summarising the policy if the species is not seen. actions (1 = Manage, 2 = Survey, 3 = Stop) and number of successive years per action.

Author(s)

Luz Pascal

Examples

## Not run: 
#values for Sumatran tigers
pen <- 0.1
p0 <- 1-pen
pem <- 0.05816
pm <- 1 - pem
V <- 175.133
Cm <- 18.784
Cs <- 10.840
d0 <- 0.01
dm <- 0.01
ds <- 0.78193

#buiding the matrices of the problem
t <- smsPOMDP::tr(p0, pm, d0, dm, ds, V, Cm, Cs) #transition matrix
o <- smsPOMDP::obs(p0, pm, d0, dm, ds, V, Cm, Cs)#observation matrix
r <- smsPOMDP::rew(p0, pm, d0, dm, ds, V, Cm, Cs)#reward matrix
state_prior <- c(1,0)

tab_actions(t, o, r, state_prior)

## End(Not run)

conservation-decisions/smsPOMDP documentation built on Oct. 27, 2020, 10:44 p.m.