sample_belief_space {pomdp}R Documentation

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.

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:

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(), plot_belief_space(), simulate_POMDP(), solve_POMDP(), solve_SARSOP(), transition_matrix(), update_belief(), write_POMDP()

Examples

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)

[Package pomdp version 1.0.3 Index]