pomdp (version 1.0.2)

sample_belief_space: 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. All states not included in the projection are held at a belief of 0. NULL means no projection.

n

size of the sample. For trajectories, it is the number of trajectories.

method

character string specifying the sampling strategy. Available are "random", "regular", "vertices", and "trajectories".

...

for the trajectory method, further arguments are passed on to simulate_POMDP(). Further arguments are ignored for the other methods.

Value

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

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).

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

• 'vertices' only samples from the vertices of the belief space.

• "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().

References

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

See Also

Other POMDP: POMDP(), plot_belief_space(), simulate_POMDP(), solve_POMDP(), solve_SARSOP(), transition_matrix(), update_belief(), write_POMDP()

Examples

# NOT RUN {
data("Tiger")

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

# sample and calculate the reward for a solved POMDP
sol <- solve_POMDP(Tiger)
samp <- sample_belief_space(sol, n = 5, method = "regular")
rew <- reward(sol, belief = samp)
cbind(samp, rew)
# }