Implemented Belief Updaters

POMDPTools provides the following generic belief updaters:

  • a discrete belief updater
  • a k previous observation updater
  • a previous observation updater
  • a nothing updater (for when the policy does not depend on any feedback)

For particle filters see ParticleFilters.jl.

Discrete (Bayesian Filter)

The DiscreteUpater is a default implementation of a discrete Bayesian filter. The DiscreteBelief type is provided to represent discrete beliefs for discrete state POMDPs.

A convenience function uniform_belief is provided to create a DiscreteBelief with equal probability for each state.

POMDPTools.BeliefUpdaters.DiscreteBeliefType
DiscreteBelief

A belief specified by a probability vector.

Normalization of b is assumed in some calculations (e.g. pdf), but it is only automatically enforced in update(...), and a warning is given if normalized incorrectly in DiscreteBelief(pomdp, b).

Constructor

DiscreteBelief(pomdp, b::Vector{Float64}; check::Bool=true)

Fields

  • pomdp : the POMDP problem
  • state_list : a vector of ordered states
  • b : the probability vector

K Previous Observations

POMDPTools.BeliefUpdaters.KMarkovUpdaterType
KMarkovUpdater

Updater that stores the k most recent observations as the belief.

Example:

up = KMarkovUpdater(5)
s0 = rand(rng, initialstate(pomdp))
initial_observation = rand(rng, initialobs(pomdp, s0))
initial_obs_vec = fill(initial_observation, 5)
hr = HistoryRecorder(rng=rng, max_steps=100)
hist = simulate(hr, pomdp, policy, up, initial_obs_vec, s0)

Previous Observation

Nothing Updater