compute_policy: compute_policy

In sarsop: Approximate POMDP Planning Software

compute_policy

Description

Derive the corresponding policy function from the alpha vectors

Usage

compute_policy(
  alpha,
  transition,
  observation,
  reward,
  state_prior = rep(1, dim(observation)[[1]])/dim(observation)[[1]],
  a_0 = 1
)

Arguments

alpha	the matrix of alpha vectors returned by sarsop
transition	Transition matrix, dimension n_s x n_s x n_a
observation	Observation matrix, dimension n_s x n_z x n_a
reward	reward matrix, dimension n_s x n_a
state_prior	initial belief state, optional, defaults to uniform over states
a_0	previous action. Belief in state depends not only on observation, but on prior belief of the state and subsequent action that had been taken.

Value

a data frame providing the optimal policy (choice of action) and corresponding value of the action for each possible belief state

Examples

m <- fisheries_matrices()
 ## Takes > 5s
if(assert_has_appl()){
alpha <- sarsop(m$transition, m$observation, m$reward, 0.95, precision = 10)
compute_policy(alpha, m$transition, m$observation, m$reward)
}

sarsop documentation built on Jan. 10, 2023, 5:16 p.m.