sample_belief_space: Sample from the Belief Space

In pomdp: Infrastructure for Partially Observable Markov Decision Processes (POMDP)

Sample from the Belief Space

Description

Sample points from belief space using a several sampling strategies.

Usage

sample_belief_space(model, projection = NULL, n = 1000, method = "random", ...)

Arguments

model	a unsolved or solved POMDP.
projection	Sample in a projected belief space. See projection() for details.
n	size of the sample. For trajectories, it is the number of trajectories.
method	character string specifying the sampling strategy. Available are "random", "regular", and "trajectories".
...	for the trajectory method, further arguments are passed on to simulate_POMDP(). Further arguments are ignored for the other methods.

Details

The purpose of sampling from the belief space is to provide good coverage or to sample belief points that are more likely to be encountered (see trajectory method). The following sampling methods are available:

• 'random' samples uniformly sample from the projected belief space using the method described by Luc Devroye (1986). Sampling is be done in parallel after a foreach backend is registered.

• 'regular' samples points using a regularly spaced grid. This method is only available for projections on 2 or 3 states.

• "trajectories" returns the belief states encountered in n trajectories of length horizon starting at the model's initial belief. Thus it returns n x horizon belief states and will contain duplicates. Projection is not supported for trajectories. Additional arguments can include the simulation horizon and the start belief which are passed on to simulate_POMDP().

Value

Returns a matrix. Each row is a sample from the belief space.

Author(s)

Michael Hahsler

References

Luc Devroye, Non-Uniform Random Variate Generation, Springer Verlag, 1986.

See Also

Other POMDP: POMDP_accessors, POMDP(), plot_belief_space(), projection(), regret(), simulate_POMDP(), solve_POMDP(), solve_SARSOP(), transition_graph(), update_belief(), value_function(), write_POMDP()

Examples

data("Tiger")

# random sampling can be done in parallel after registering a backend.
# doparallel::registerDoParallel()

sample_belief_space(Tiger, n = 5)
sample_belief_space(Tiger, n = 5, method = "regular")
sample_belief_space(Tiger, n = 1, horizon = 5, method = "trajectories")

# sample, determine the optimal action and calculate the expected reward for a solved POMDP
# Note: check.names = FALSE is used to preserve the `-` for the state names in the dataframe.
sol <- solve_POMDP(Tiger)
samp <- sample_belief_space(sol, n = 5, method = "regular")
data.frame(samp, action = optimal_action(sol,  belief = samp), 
  reward = reward(sol, belief = samp), check.names = FALSE)
  
# sample from a 3 state problem
data(Three_doors)
Three_doors

sample_belief_space(Three_doors, n = 5)
sample_belief_space(Three_doors, n = 5, projection = c(`tiger-left` = .1))

if ("Ternary" %in% installed.packages()) {
sample_belief_space(Three_doors, n = 9, method = "regular")
sample_belief_space(Three_doors, n = 9, method = "regular", projection = c(`tiger-left` = .1))
}

sample_belief_space(Three_doors, n = 1, horizon = 5, method = "trajectories")

pomdp documentation built on Sept. 9, 2023, 1:07 a.m.